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https://github.com/leondgarse/keras_cv_attention_models
Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam
https://github.com/leondgarse/keras_cv_attention_models
attention clip coco ddpm detection imagenet keras model recognition segment-anything stable-diffusion tensorflow tf tf2 visualizing
Last synced: 2 days ago
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Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam
- Host: GitHub
- URL: https://github.com/leondgarse/keras_cv_attention_models
- Owner: leondgarse
- License: mit
- Created: 2021-08-02T00:59:55.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-12T05:25:23.000Z (4 months ago)
- Last Synced: 2024-09-07T09:35:41.211Z (2 months ago)
- Topics: attention, clip, coco, ddpm, detection, imagenet, keras, model, recognition, segment-anything, stable-diffusion, tensorflow, tf, tf2, visualizing
- Language: Python
- Homepage:
- Size: 4.22 MB
- Stars: 587
- Watchers: 23
- Forks: 92
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ___Keras_cv_attention_models___
***
- **WARNING: currently NOT compatible with `keras 3.x`, if using `tensorflow>=2.16.0`, needs to install `pip install tf-keras~=$(pip show tensorflow | awk -F ': ' '/Version/{print $2}')` manually. While importing, import this package ahead of Tensorflow, or set `export TF_USE_LEGACY_KERAS=1`.**
- **It's not recommended downloading and loading model from h5 file directly, better building model and loading weights like `import kecam; mm = kecam.models.LCNet050()`.**
- **coco_train_script.py for TF is still under testing...**
- [___>>>> Roadmap and todo list <<<<___](https://github.com/leondgarse/keras_cv_attention_models/wiki/Roadmap)
- [General Usage](#general-usage)
- [Basic](#basic)
- [T4 Inference](#t4-inference)
- [Layers](#layers)
- [Model surgery](#model-surgery)
- [ImageNet training and evaluating](#imagenet-training-and-evaluating)
- [COCO training and evaluating](#coco-training-and-evaluating)
- [CLIP training and evaluating](#clip-training-and-evaluating)
- [Text training](#text-training)
- [DDPM training](#ddpm-training)
- [Visualizing](#visualizing)
- [TFLite Conversion](#tflite-conversion)
- [Using PyTorch as backend](#using-pytorch-as-backend)
- [Using keras core as backend](#using-keras-core-as-backend)
- [Recognition Models](#recognition-models)
- [AotNet](#aotnet)
- [BEiT](#beit)
- [BEiTV2](#beitv2)
- [BotNet](#botnet)
- [CAFormer](#caformer)
- [CMT](#cmt)
- [CoaT](#coat)
- [CoAtNet](#coatnet)
- [ConvNeXt](#convnext)
- [ConvNeXtV2](#convnextv2)
- [CoTNet](#cotnet)
- [CSPNeXt](#cspnext)
- [DaViT](#davit)
- [DiNAT](#dinat)
- [DINOv2](#dinov2)
- [EdgeNeXt](#edgenext)
- [EfficientFormer](#efficientformer)
- [EfficientFormerV2](#efficientformerv2)
- [EfficientNet](#efficientnet)
- [EfficientNetEdgeTPU](#efficientnetedgetpu)
- [EfficientNetV2](#efficientnetv2)
- [EfficientViT_B](#efficientvit_b)
- [EfficientViT_M](#efficientvit_m)
- [EVA](#eva)
- [EVA02](#eva02)
- [FasterNet](#fasternet)
- [FasterViT](#fastervit)
- [FastViT](#fastvit)
- [FBNetV3](#fbnetv3)
- [FlexiViT](#flexivit)
- [GCViT](#gcvit)
- [GhostNet](#ghostnet)
- [GhostNetV2](#ghostnetv2)
- [GMLP](#gmlp)
- [GPViT](#gpvit)
- [HaloNet](#halonet)
- [Hiera](#hiera)
- [HorNet](#hornet)
- [IFormer](#iformer)
- [InceptionNeXt](#inceptionnext)
- [LCNet](#lcnet)
- [LeViT](#levit)
- [MaxViT](#maxvit)
- [MetaTransFormer](#metatransformer)
- [MLP mixer](#mlp-mixer)
- [MobileNetV3](#mobilenetv3)
- [MobileViT](#mobilevit)
- [MobileViT_V2](#mobilevit_v2)
- [MogaNet](#moganet)
- [NAT](#nat)
- [NFNets](#nfnets)
- [PVT_V2](#pvt_v2)
- [RegNetY](#regnety)
- [RegNetZ](#regnetz)
- [RepViT](#repvit)
- [ResMLP](#resmlp)
- [ResNeSt](#resnest)
- [ResNetD](#resnetd)
- [ResNetQ](#resnetq)
- [ResNeXt](#resnext)
- [SwinTransformerV2](#swintransformerv2)
- [TinyNet](#tinynet)
- [TinyViT](#tinyvit)
- [UniFormer](#uniformer)
- [VanillaNet](#vanillanet)
- [VOLO](#volo)
- [WaveMLP](#wavemlp)
- [Detection Models](#detection-models)
- [EfficientDet](#efficientdet)
- [YOLO_NAS](#yolo_nas)
- [YOLOR](#yolor)
- [YOLOV7](#yolov7)
- [YOLOV8](#yolov8)
- [YOLOX](#yolox)
- [Language Models](#language-models)
- [GPT2](#gpt2)
- [LLaMA2](#llama2)
- [Stable Diffusion](#stable-diffusion)
- [Segmentation Models](#segmentation-models)
- [YOLOV8 Segmentation](#yolov8-segmentation)
- [Segment Anything](#segment-anything)
- [Licenses](#licenses)
- [Citing](#citing)
***
# General Usage
## Basic
- **Default import** will not specific these while using them in READMEs.
```py
import os
import sys
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow import keras
```
- Install as pip package. `kecam` is a short alias name of this package. **Note**: the pip package `kecam` doesn't set any backend requirement, make sure either Tensorflow or PyTorch installed before hand. For PyTorch backend usage, refer [Keras PyTorch Backend](keras_cv_attention_models/pytorch_backend).
```sh
pip install -U kecam
# Or
pip install -U keras-cv-attention-models
# Or
pip install -U git+https://github.com/leondgarse/keras_cv_attention_models
```
Refer to each sub directory for detail usage.
- **Basic model prediction**
```py
from keras_cv_attention_models import volo
mm = volo.VOLO_d1(pretrained="imagenet")
""" Run predict """
import tensorflow as tf
from tensorflow import keras
from keras_cv_attention_models.test_images import cat
img = cat()
imm = keras.applications.imagenet_utils.preprocess_input(img, mode='torch')
pred = mm(tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)).numpy()
pred = tf.nn.softmax(pred).numpy() # If classifier activation is not softmax
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.99664897),
# ('n02123045', 'tabby', 0.0007249644),
# ('n02123159', 'tiger_cat', 0.00020345),
# ('n02127052', 'lynx', 5.4973923e-05),
# ('n02123597', 'Siamese_cat', 2.675306e-05)]
```
Or just use model preset `preprocess_input` and `decode_predictions`
```py
from keras_cv_attention_models import coatnet
mm = coatnet.CoAtNet0()
from keras_cv_attention_models.test_images import cat
preds = mm(mm.preprocess_input(cat()))
print(mm.decode_predictions(preds))
# [[('n02124075', 'Egyptian_cat', 0.9999875), ('n02123045', 'tabby', 5.194884e-06), ...]]
```
The preset `preprocess_input` and `decode_predictions` also compatible with PyTorch backend.
```py
os.environ['KECAM_BACKEND'] = 'torch'
from keras_cv_attention_models import caformer
mm = caformer.CAFormerS18()
# >>>> Using PyTorch backend
# >>>> Aligned input_shape: [3, 224, 224]
# >>>> Load pretrained from: ~/.keras/models/caformer_s18_224_imagenet.h5
from keras_cv_attention_models.test_images import cat
preds = mm(mm.preprocess_input(cat()))
print(preds.shape)
# torch.Size([1, 1000])
print(mm.decode_predictions(preds))
# [[('n02124075', 'Egyptian_cat', 0.8817097), ('n02123045', 'tabby', 0.009335292), ...]]
```
- **`num_classes=0`** set for excluding model top `GlobalAveragePooling2D + Dense` layers.
```py
from keras_cv_attention_models import resnest
mm = resnest.ResNest50(num_classes=0)
print(mm.output_shape)
# (None, 7, 7, 2048)
```
- **`num_classes={custom output classes}`** others than `1000` or `0` will just skip loading the header Dense layer weights. As `model.load_weights(weight_file, by_name=True, skip_mismatch=True)` is used for loading weights.
```py
from keras_cv_attention_models import swin_transformer_v2
mm = swin_transformer_v2.SwinTransformerV2Tiny_window8(num_classes=64)
# >>>> Load pretrained from: ~/.keras/models/swin_transformer_v2_tiny_window8_256_imagenet.h5
# WARNING:tensorflow:Skipping loading weights for layer #601 (named predictions) due to mismatch in shape for weight predictions/kernel:0. Weight expects shape (768, 64). Received saved weight with shape (768, 1000)
# WARNING:tensorflow:Skipping loading weights for layer #601 (named predictions) due to mismatch in shape for weight predictions/bias:0. Weight expects shape (64,). Received saved weight with shape (1000,)
```
- **Reload own model weights by set `pretrained="xxx.h5"`**. Better than calling `model.load_weights` directly, if reloading model with different `input_shape` and with weights shape not matching.
```py
import os
from keras_cv_attention_models import coatnet
pretrained = os.path.expanduser('~/.keras/models/coatnet0_224_imagenet.h5')
mm = coatnet.CoAtNet1(input_shape=(384, 384, 3), pretrained=pretrained) # No sense, just showing usage
```
- **Alias name `kecam`** can be used instead of `keras_cv_attention_models`. It's `__init__.py` only with `from keras_cv_attention_models import *`.
```py
import kecam
mm = kecam.yolor.YOLOR_CSP()
imm = kecam.test_images.dog_cat()
preds = mm(mm.preprocess_input(imm))
bboxs, lables, confidences = mm.decode_predictions(preds)[0]
kecam.coco.show_image_with_bboxes(imm, bboxs, lables, confidences)
```
- **Calculate flops** method from [TF 2.0 Feature: Flops calculation #32809](https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-849439287). For PyTorch backend, needs `thop` `pip install thop`.
```py
from keras_cv_attention_models import coatnet, resnest, model_surgery
model_surgery.get_flops(coatnet.CoAtNet0())
# >>>> FLOPs: 4,221,908,559, GFLOPs: 4.2219G
model_surgery.get_flops(resnest.ResNest50())
# >>>> FLOPs: 5,378,399,992, GFLOPs: 5.3784G
```
- **[Deprecated] `tensorflow_addons`** is not imported by default. While reloading model depending on `GroupNormalization` like `MobileViTV2` from `h5` directly, needs to import `tensorflow_addons` manually first.
```py
import tensorflow_addons as tfa
model_path = os.path.expanduser('~/.keras/models/mobilevit_v2_050_256_imagenet.h5')
mm = keras.models.load_model(model_path)
```
- **Export TF model to onnx**. Needs `tf2onnx` for TF, `pip install onnx tf2onnx onnxsim onnxruntime`. For using PyTorch backend, exporting onnx is supported by PyTorch.
```py
from keras_cv_attention_models import volo, nat, model_surgery
mm = nat.DiNAT_Small(pretrained=True)
model_surgery.export_onnx(mm, fuse_conv_bn=True, batch_size=1, simplify=True)
# Exported simplified onnx: dinat_small.onnx
# Run test
from keras_cv_attention_models.imagenet import eval_func
aa = eval_func.ONNXModelInterf(mm.name + '.onnx')
inputs = np.random.uniform(size=[1, *mm.input_shape[1:]]).astype('float32')
print(f"{np.allclose(aa(inputs), mm(inputs), atol=1e-5) = }")
# np.allclose(aa(inputs), mm(inputs), atol=1e-5) = True
```
- **Model summary** `model_summary.csv` contains gathered model info.
- `params` for model params count in `M`
- `flops` for FLOPs in `G`
- `input` for model input shape
- `acc_metrics` means `Imagenet Top1 Accuracy` for recognition models, `COCO val AP` for detection models
- `inference_qps` for `T4 inference query per second` with `batch_size=1 + trtexec`
- `extra` means if any extra training info.
```py
from keras_cv_attention_models import plot_func
plot_series = [
"efficientnetv2", 'tinynet', 'lcnet', 'mobilenetv3', 'fasternet', 'fastervit', 'ghostnet',
'inceptionnext', 'efficientvit_b', 'mobilevit', 'convnextv2', 'efficientvit_m', 'hiera',
]
plot_func.plot_model_summary(
plot_series, model_table="model_summary.csv", log_scale_x=True, allow_extras=['mae_in1k_ft1k']
)
```
- **Code format** is using `line-length=160`:
```sh
find ./* -name "*.py" | grep -v __init__ | xargs -I {} black -l 160 {}
```
## T4 Inference
- **T4 Inference** in the model tables are tested using `trtexec` on `Tesla T4` with `CUDA=12.0.1-1, Driver=525.60.13`. All models are exported as ONNX using PyTorch backend, using `batch_szie=1` only. **Note: this data is for reference only, and vary in different batch sizes or benchmark tools or platforms or implementations**.
- All results are tested using colab [trtexec.ipynb](https://colab.research.google.com/drive/1xLwfvbZNqadkdAZu9b0UzOrETLo657oc?usp=drive_link). Thus reproducible by any others.
```py
os.environ["KECAM_BACKEND"] = "torch"
from keras_cv_attention_models import convnext, test_images, imagenet
# >>>> Using PyTorch backend
mm = convnext.ConvNeXtTiny()
mm.export_onnx(simplify=True)
# Exported onnx: convnext_tiny.onnx
# Running onnxsim.simplify...
# Exported simplified onnx: convnext_tiny.onnx
# Onnx run test
tt = imagenet.eval_func.ONNXModelInterf('convnext_tiny.onnx')
print(mm.decode_predictions(tt(mm.preprocess_input(test_images.cat()))))
# [[('n02124075', 'Egyptian_cat', 0.880507), ('n02123045', 'tabby', 0.0047998047), ...]]
""" Run trtexec benchmark """
!trtexec --onnx=convnext_tiny.onnx --fp16 --allowGPUFallback --useSpinWait --useCudaGraph
```
## Layers
- [attention_layers](keras_cv_attention_models/attention_layers) is `__init__.py` only, which imports core layers defined in model architectures. Like `RelativePositionalEmbedding` from `botnet`, `outlook_attention` from `volo`, and many other `Positional Embedding Layers` / `Attention Blocks`.
```py
from keras_cv_attention_models import attention_layers
aa = attention_layers.RelativePositionalEmbedding()
print(f"{aa(tf.ones([1, 4, 14, 16, 256])).shape = }")
# aa(tf.ones([1, 4, 14, 16, 256])).shape = TensorShape([1, 4, 14, 16, 14, 16])
```
## Model surgery
- [model_surgery](keras_cv_attention_models/model_surgery) including functions used to change model parameters after built.
```py
from keras_cv_attention_models import model_surgery
mm = keras.applications.ResNet50() # Trainable params: 25,583,592
# Replace all ReLU with PReLU. Trainable params: 25,606,312
mm = model_surgery.replace_ReLU(mm, target_activation='PReLU')
# Fuse conv and batch_norm layers. Trainable params: 25,553,192
mm = model_surgery.convert_to_fused_conv_bn_model(mm)
```
## ImageNet training and evaluating
- [ImageNet](keras_cv_attention_models/imagenet) contains more detail usage and some comparing results.
- [Init Imagenet dataset using tensorflow_datasets #9](https://github.com/leondgarse/keras_cv_attention_models/discussions/9).
- For custom dataset, `custom_dataset_script.py` can be used creating a `json` format file, which can be used as `--data_name xxx.json` for training, detail usage can be found in [Custom recognition dataset](https://github.com/leondgarse/keras_cv_attention_models/discussions/52#discussion-3971513).
- Another method creating custom dataset is using `tfds.load`, refer [Writing custom datasets](https://www.tensorflow.org/datasets/add_dataset) and [Creating private tensorflow_datasets from tfds #48](https://github.com/leondgarse/keras_cv_attention_models/discussions/48) by @Medicmind.
- Running an AWS Sagemaker estimator job using `keras_cv_attention_models` can be found in [AWS Sagemaker script example](https://github.com/leondgarse/keras_cv_attention_models/discussions/107) by @Medicmind.
- `aotnet.AotNet50` default parameters set is a typical `ResNet50` architecture with `Conv2D use_bias=False` and `padding` like `PyTorch`.
- Default parameters for `train_script.py` is like `A3` configuration from [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/pdf/2110.00476.pdf) with `batch_size=256, input_shape=(160, 160)`.
```sh
# `antialias` is default enabled for resize, can be turned off be set `--disable_antialias`.
CUDA_VISIBLE_DEVICES='0' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 train_script.py --seed 0 -s aotnet50
```
```sh
# Evaluation using input_shape (224, 224).
# `antialias` usage should be same with training.
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m aotnet50_epoch_103_val_acc_0.7674.h5 -i 224 --central_crop 0.95
# >>>> Accuracy top1: 0.78466 top5: 0.94088
```
- **Restore from break point** by setting `--restore_path` and `--initial_epoch`, and keep other parameters same. `restore_path` is higher priority than `model` and `additional_model_kwargs`, also restore `optimizer` and `loss`. `initial_epoch` is mainly for learning rate scheduler. If not sure where it stopped, check `checkpoints/{save_name}_hist.json`.
```py
import json
with open("checkpoints/aotnet50_hist.json", "r") as ff:
aa = json.load(ff)
len(aa['lr'])
# 41 ==> 41 epochs are finished, initial_epoch is 41 then, restart from epoch 42
```
```sh
CUDA_VISIBLE_DEVICES='0' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 train_script.py --seed 0 -r checkpoints/aotnet50_latest.h5 -I 41
# >>>> Restore model from: checkpoints/aotnet50_latest.h5
# Epoch 42/105
```
- **`eval_script.py`** is used for evaluating model accuracy. [EfficientNetV2 self tested imagenet accuracy #19](https://github.com/leondgarse/keras_cv_attention_models/discussions/19) just showing how different parameters affecting model accuracy.
```sh
# evaluating pretrained builtin model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m regnet.RegNetZD8
# evaluating pretrained timm model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m timm.models.resmlp_12_224 --input_shape 224
# evaluating specific h5 model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m checkpoints/xxx.h5
# evaluating specific tflite model
CUDA_VISIBLE_DEVICES='1' python3 eval_script.py -m xxx.tflite
```
- **Progressive training** refer to [PDF 2104.00298 EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/pdf/2104.00298.pdf). AotNet50 A3 progressive input shapes `96 128 160`:
```sh
CUDA_VISIBLE_DEVICES='1' TF_XLA_FLAGS="--tf_xla_auto_jit=2" python3 progressive_train_script.py \
--progressive_epochs 33 66 -1 \
--progressive_input_shapes 96 128 160 \
--progressive_magnitudes 2 4 6 \
-s aotnet50_progressive_3_lr_steps_100 --seed 0
```
- Transfer learning with `freeze_backbone` or `freeze_norm_layers`: [EfficientNetV2B0 transfer learning on cifar10 testing freezing backbone #55](https://github.com/leondgarse/keras_cv_attention_models/discussions/55).
- [Token label train test on CIFAR10 #57](https://github.com/leondgarse/keras_cv_attention_models/discussions/57). **Currently not working as well as expected**. `Token label` is implementation of [Github zihangJiang/TokenLabeling](https://github.com/zihangJiang/TokenLabeling), paper [PDF 2104.10858 All Tokens Matter: Token Labeling for Training Better Vision Transformers](https://arxiv.org/pdf/2104.10858.pdf).
## COCO training and evaluating
- **Currently still under testing**.
- [COCO](keras_cv_attention_models/coco) contains more detail usage.
- `custom_dataset_script.py` can be used creating a `json` format file, which can be used as `--data_name xxx.json` for training, detail usage can be found in [Custom detection dataset](https://github.com/leondgarse/keras_cv_attention_models/discussions/52#discussioncomment-2460664).
- Default parameters for `coco_train_script.py` is `EfficientDetD0` with `input_shape=(256, 256, 3), batch_size=64, mosaic_mix_prob=0.5, freeze_backbone_epochs=32, total_epochs=105`. Technically, it's any `pyramid structure backbone` + `EfficientDet / YOLOX header / YOLOR header` + `anchor_free / yolor / efficientdet anchors` combination supported.
- Currently 4 types anchors supported, parameter **`anchors_mode`** controls which anchor to use, value in `["efficientdet", "anchor_free", "yolor", "yolov8"]`. Default `None` for `det_header` presets.
- **NOTE: `YOLOV8` has a default `regression_len=64` for bbox output length. Typically it's `4` for other detection models, for yolov8 it's `reg_max=16 -> regression_len = 16 * 4 == 64`.**
| anchors_mode | use_object_scores | num_anchors | anchor_scale | aspect_ratios | num_scales | grid_zero_start |
| ------------ | ----------------- | ----------- | ------------ | ------------- | ---------- | --------------- |
| efficientdet | False | 9 | 4 | [1, 2, 0.5] | 3 | False |
| anchor_free | True | 1 | 1 | [1] | 1 | True |
| yolor | True | 3 | None | presets | None | offset=0.5 |
| yolov8 | False | 1 | 1 | [1] | 1 | False |
```sh
# Default EfficientDetD0
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py
# Default EfficientDetD0 using input_shape 512, optimizer adamw, freezing backbone 16 epochs, total 50 + 5 epochs
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py -i 512 -p adamw --freeze_backbone_epochs 16 --lr_decay_steps 50
# EfficientNetV2B0 backbone + EfficientDetD0 detection header
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone efficientnet.EfficientNetV2B0 --det_header efficientdet.EfficientDetD0
# ResNest50 backbone + EfficientDetD0 header using yolox like anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone resnest.ResNest50 --anchors_mode anchor_free
# UniformerSmall32 backbone + EfficientDetD0 header using yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone uniformer.UniformerSmall32 --anchors_mode yolor
# Typical YOLOXS with anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolox.YOLOXS --freeze_backbone_epochs 0
# YOLOXS with efficientdet anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolox.YOLOXS --anchors_mode efficientdet --freeze_backbone_epochs 0
# CoAtNet0 backbone + YOLOX header with yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone coatnet.CoAtNet0 --det_header yolox.YOLOX --anchors_mode yolor
# Typical YOLOR_P6 with yolor anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolor.YOLOR_P6 --freeze_backbone_epochs 0
# YOLOR_P6 with anchor_free anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --det_header yolor.YOLOR_P6 --anchors_mode anchor_free --freeze_backbone_epochs 0
# ConvNeXtTiny backbone + YOLOR header with efficientdet anchors
CUDA_VISIBLE_DEVICES='0' python3 coco_train_script.py --backbone convnext.ConvNeXtTiny --det_header yolor.YOLOR --anchors_mode yolor
```
**Note: COCO training still under testing, may change parameters and default behaviors. Take the risk if would like help developing.**
- **`coco_eval_script.py`** is used for evaluating model AP / AR on COCO validation set. It has a dependency `pip install pycocotools` which is not in package requirements. More usage can be found in [COCO Evaluation](keras_cv_attention_models/coco#evaluation).
```sh
# EfficientDetD0 using resize method bilinear w/o antialias
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m efficientdet.EfficientDetD0 --resize_method bilinear --disable_antialias
# >>>> [COCOEvalCallback] input_shape: (512, 512), pyramid_levels: [3, 7], anchors_mode: efficientdet
# YOLOX using BGR input format
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m yolox.YOLOXTiny --use_bgr_input --nms_method hard --nms_iou_or_sigma 0.65
# >>>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: anchor_free
# YOLOR / YOLOV7 using letterbox_pad and other tricks.
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m yolor.YOLOR_CSP --nms_method hard --nms_iou_or_sigma 0.65 \
--nms_max_output_size 300 --nms_topk -1 --letterbox_pad 64 --input_shape 704
# >>>> [COCOEvalCallback] input_shape: (704, 704), pyramid_levels: [3, 5], anchors_mode: yolor
# Specify h5 model
CUDA_VISIBLE_DEVICES='1' python3 coco_eval_script.py -m checkpoints/yoloxtiny_yolor_anchor.h5
# >>>> [COCOEvalCallback] input_shape: (416, 416), pyramid_levels: [3, 5], anchors_mode: yolor
```
- **[Experimental] Training using PyTorch backend**
```py
import os, sys, torch
os.environ["KECAM_BACKEND"] = "torch"
from keras_cv_attention_models.yolov8 import train, yolov8
from keras_cv_attention_models import efficientnet
global_device = torch.device("cuda:0") if torch.cuda.is_available() and int(os.environ.get("CUDA_VISIBLE_DEVICES", "0")) >= 0 else torch.device("cpu")
# model Trainable params: 7,023,904, GFLOPs: 8.1815G
bb = efficientnet.EfficientNetV2B0(input_shape=(3, 640, 640), num_classes=0)
model = yolov8.YOLOV8_N(backbone=bb, classifier_activation=None, pretrained=None).to(global_device) # Note: classifier_activation=None
# model = yolov8.YOLOV8_N(input_shape=(3, None, None), classifier_activation=None, pretrained=None).to(global_device)
ema = train.train(model, dataset_path="coco.json", initial_epoch=0)
```
## CLIP training and evaluating
- [CLIP](keras_cv_attention_models/clip) contains more detail usage.
- `custom_dataset_script.py` can be used creating a `tsv` / `json` format file, which can be used as `--data_name xxx.tsv` for training, detail usage can be found in [Custom caption dataset](https://github.com/leondgarse/keras_cv_attention_models/discussions/52#discussioncomment-6516154).
- **Train using `clip_train_script.py on COCO captions`** Default `--data_path` is a testing one `datasets/coco_dog_cat/captions.tsv`.
```sh
CUDA_VISIBLE_DEVICES=1 TF_XLA_FLAGS="--tf_xla_auto_jit=2" python clip_train_script.py -i 160 -b 128 \
--text_model_pretrained None --data_path coco_captions.tsv
```
**Train Using PyTorch backend by setting `KECAM_BACKEND='torch'`**
```sh
KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python clip_train_script.py -i 160 -b 128 \
--text_model_pretrained None --data_path coco_captions.tsv
```
## Text training
- Currently it's only a simple one modified from [Github karpathy/nanoGPT](https://github.com/karpathy/nanoGPT).
- **Train using `text_train_script.py`** As dataset is randomly sampled, needs to specify `steps_per_epoch`
```sh
CUDA_VISIBLE_DEVICES=1 TF_XLA_FLAGS="--tf_xla_auto_jit=2" python text_train_script.py -m LLaMA2_15M \
--steps_per_epoch 8000 --batch_size 8 --tokenizer SentencePieceTokenizer
```
**Train Using PyTorch backend by setting `KECAM_BACKEND='torch'`**
```sh
KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python text_train_script.py -m LLaMA2_15M \
--steps_per_epoch 8000 --batch_size 8 --tokenizer SentencePieceTokenizer
```
**Plotting**
```py
from keras_cv_attention_models import plot_func
hists = ['checkpoints/text_llama2_15m_tensorflow_hist.json', 'checkpoints/text_llama2_15m_torch_hist.json']
plot_func.plot_hists(hists, addition_plots=['val_loss', 'lr'], skip_first=3)
```
## DDPM training
- [Stable Diffusion](keras_cv_attention_models/stable_diffusion) contains more detail usage.
- **Note: Works better with PyTorch backend, Tensorflow one seems overfitted if training logger like `--epochs 200`, and evaluation runs ~5 times slower. [???]**
- **Dataset** can be a directory containing images for basic DDPM training using images only, or a recognition json file created following [Custom recognition dataset](https://github.com/leondgarse/keras_cv_attention_models/discussions/52#discussion-3971513), which will train using labels as instruction.
```sh
python custom_dataset_script.py --train_images cifar10/train/ --test_images cifar10/test/
# >>>> total_train_samples: 50000, total_test_samples: 10000, num_classes: 10
# >>>> Saved to: cifar10.json
```
- **Train using `ddpm_train_script.py on cifar10 with labels`** Default `--data_path` is builtin `cifar10`.
```py
# Set --eval_interval 50 as TF evaluation is rather slow [???]
TF_XLA_FLAGS="--tf_xla_auto_jit=2" CUDA_VISIBLE_DEVICES=1 python ddpm_train_script.py --eval_interval 50
```
**Train Using PyTorch backend by setting `KECAM_BACKEND='torch'`**
```py
KECAM_BACKEND='torch' CUDA_VISIBLE_DEVICES=1 python ddpm_train_script.py
```
## Visualizing
- [Visualizing](keras_cv_attention_models/visualizing) is for visualizing convnet filters or attention map scores.
- **make_and_apply_gradcam_heatmap** is for Grad-CAM class activation visualization.
```py
from keras_cv_attention_models import visualizing, test_images, resnest
mm = resnest.ResNest50()
img = test_images.dog()
superimposed_img, heatmap, preds = visualizing.make_and_apply_gradcam_heatmap(mm, img, layer_name="auto")
```
- **plot_attention_score_maps** is model attention score maps visualization.
```py
from keras_cv_attention_models import visualizing, test_images, botnet
img = test_images.dog()
_ = visualizing.plot_attention_score_maps(botnet.BotNetSE33T(), img)
```
## TFLite Conversion
- Currently `TFLite` not supporting `tf.image.extract_patches` / `tf.transpose with len(perm) > 4`. Some operations could be supported in latest or `tf-nightly` version, like previously not supported `gelu` / `Conv2D with groups>1` are working now. May try if encountering issue.
- More discussion can be found [Converting a trained keras CV attention model to TFLite #17](https://github.com/leondgarse/keras_cv_attention_models/discussions/17). Some speed testing results can be found [How to speed up inference on a quantized model #44](https://github.com/leondgarse/keras_cv_attention_models/discussions/44#discussioncomment-2348910).
- Functions like `model_surgery.convert_groups_conv2d_2_split_conv2d` and `model_surgery.convert_gelu_to_approximate` are not needed using up-to-date TF version.
- Not supporting `VOLO` / `HaloNet` models converting, cause they need a longer `tf.transpose` `perm`.
- **model_surgery.convert_dense_to_conv** converts all `Dense` layer with 3D / 4D inputs to `Conv1D` / `Conv2D`, as currently TFLite xnnpack not supporting it.
```py
from keras_cv_attention_models import beit, model_surgery, efficientformer, mobilevit
mm = efficientformer.EfficientFormerL1()
mm = model_surgery.convert_dense_to_conv(mm) # Convert all Dense layers
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())
```
| Model | Dense, use_xnnpack=false | Conv, use_xnnpack=false | Conv, use_xnnpack=true |
| ----------------- | ------------------------- | ------------------------- | ------------------------- |
| MobileViT_S | Inference (avg) 215371 us | Inference (avg) 163836 us | Inference (avg) 163817 us |
| EfficientFormerL1 | Inference (avg) 126829 us | Inference (avg) 107053 us | Inference (avg) 107132 us |
- **model_surgery.convert_extract_patches_to_conv** converts `tf.image.extract_patches` to a `Conv2D` version:
```py
from keras_cv_attention_models import cotnet, model_surgery
from keras_cv_attention_models.imagenet import eval_func
mm = cotnet.CotNetSE50D()
mm = model_surgery.convert_groups_conv2d_2_split_conv2d(mm)
# mm = model_surgery.convert_gelu_to_approximate(mm) # Not required if using up-to-date TFLite
mm = model_surgery.convert_extract_patches_to_conv(mm)
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())
test_inputs = np.random.uniform(size=[1, *mm.input_shape[1:]])
print(np.allclose(mm(test_inputs), eval_func.TFLiteModelInterf(mm.name + '.tflite')(test_inputs), atol=1e-7))
# True
```
- **model_surgery.prepare_for_tflite** is just a combination of above functions:
```py
from keras_cv_attention_models import beit, model_surgery
mm = beit.BeitBasePatch16()
mm = model_surgery.prepare_for_tflite(mm)
converter = tf.lite.TFLiteConverter.from_keras_model(mm)
open(mm.name + ".tflite", "wb").write(converter.convert())
```
- **Detection models** including `efficinetdet` / `yolox` / `yolor`, model can be converted a TFLite format directly. If need [DecodePredictions](https://github.com/leondgarse/keras_cv_attention_models/blob/main/keras_cv_attention_models/coco/eval_func.py#L8) also included in TFLite model, need to set `use_static_output=True` for `DecodePredictions`, as TFLite requires a more static output shape. Model output shape will be fixed as `[batch, max_output_size, 6]`. The last dimension `6` means `[bbox_top, bbox_left, bbox_bottom, bbox_right, label_index, confidence]`, and those valid ones are where `confidence > 0`.
```py
""" Init model """
from keras_cv_attention_models import efficientdet
model = efficientdet.EfficientDetD0(pretrained="coco")
""" Create a model with DecodePredictions using `use_static_output=True` """
model.decode_predictions.use_static_output = True
# parameters like score_threshold / iou_or_sigma can be set another value if needed.
nn = model.decode_predictions(model.outputs[0], score_threshold=0.5)
bb = keras.models.Model(model.inputs[0], nn)
""" Convert TFLite """
converter = tf.lite.TFLiteConverter.from_keras_model(bb)
open(bb.name + ".tflite", "wb").write(converter.convert())
""" Inference test """
from keras_cv_attention_models.imagenet import eval_func
from keras_cv_attention_models import test_images
dd = eval_func.TFLiteModelInterf(bb.name + ".tflite")
imm = test_images.cat()
inputs = tf.expand_dims(tf.image.resize(imm, dd.input_shape[1:-1]), 0)
inputs = keras.applications.imagenet_utils.preprocess_input(inputs, mode='torch')
preds = dd(inputs)[0]
print(f"{preds.shape = }")
# preds.shape = (100, 6)
pred = preds[preds[:, -1] > 0]
bboxes, labels, confidences = pred[:, :4], pred[:, 4], pred[:, -1]
print(f"{bboxes = }, {labels = }, {confidences = }")
# bboxes = array([[0.22825494, 0.47238672, 0.816262 , 0.8700745 ]], dtype=float32),
# labels = array([16.], dtype=float32),
# confidences = array([0.8309707], dtype=float32)
""" Show result """
from keras_cv_attention_models.coco import data
data.show_image_with_bboxes(imm, bboxes, labels, confidences, num_classes=90)
```
## Using PyTorch as backend
- **Experimental** [Keras PyTorch Backend](keras_cv_attention_models/pytorch_backend).
- **Set os environment `export KECAM_BACKEND='torch'` to enable this PyTorch backend.**
- Currently supports most recognition and detection models except hornet*gf / nfnets / volo. For detection models, using `torchvision.ops.nms` while running prediction.
- **Basic model build and prediction**.
- Will load same `h5` weights as TF one if available.
- Note: `input_shape` will auto fit image data format. Given `input_shape=(224, 224, 3)` or `input_shape=(3, 224, 224)`, will both set to `(3, 224, 224)` if `channels_first`.
- Note: model is default set to `eval` mode.
```py
os.environ['KECAM_BACKEND'] = 'torch'
from keras_cv_attention_models import res_mlp
mm = res_mlp.ResMLP12()
# >>>> Load pretrained from: ~/.keras/models/resmlp12_imagenet.h5
print(f"{mm.input_shape = }")
# mm.input_shape = [None, 3, 224, 224]
import torch
print(f"{isinstance(mm, torch.nn.Module) = }")
# isinstance(mm, torch.nn.Module) = True
# Run prediction
from keras_cv_attention_models.test_images import cat
print(mm.decode_predictions(mm(mm.preprocess_input(cat())))[0])
# [('n02124075', 'Egyptian_cat', 0.9597896), ('n02123045', 'tabby', 0.012809471), ...]
```
- **Export typical PyTorch onnx / pth**.
```py
import torch
torch.onnx.export(mm, torch.randn(1, 3, *mm.input_shape[2:]), mm.name + ".onnx")
# Or by export_onnx
mm.export_onnx()
# Exported onnx: resmlp12.onnx
mm.export_pth()
# Exported pth: resmlp12.pth
```
- **Save weights as h5**. This `h5` can also be loaded in typical TF backend model. Currently it's only weights without model structure supported.
```py
mm.save_weights("foo.h5")
```
- **Training with compile and fit** Note: loss function arguments should be `y_true, y_pred`, while typical torch loss functions using `y_pred, y_true`.
```py
import torch
from keras_cv_attention_models.backend import models, layers
mm = models.Sequential([layers.Input([3, 32, 32]), layers.Conv2D(32, 3), layers.GlobalAveragePooling2D(), layers.Dense(10)])
if torch.cuda.is_available():
_ = mm.to("cuda")
xx = torch.rand([64, *mm.input_shape[1:]])
yy = torch.functional.F.one_hot(torch.randint(0, mm.output_shape[-1], size=[64]), mm.output_shape[-1]).float()
loss = lambda y_true, y_pred: (y_true - y_pred.float()).abs().mean()
# Will check kwargs for calling `self.train_compile` or `torch.nn.Module.compile`
mm.compile(optimizer="AdamW", loss=loss, metrics='acc', grad_accumulate=4)
mm.fit(xx, yy, epochs=2, batch_size=4)
```
## Using keras core as backend
- **[Experimental] Set os environment `export KECAM_BACKEND='keras_core'` to enable this `keras_core` backend. Not using `keras>3.0`, as still not compiling with TensorFlow==2.15.0**
- `keras-core` has its own backends, supporting tensorflow / torch / jax, by editting `~/.keras/keras.json` `"backend"` value.
- Currently most recognition models except `HaloNet` / `BotNet` supported, also `GPT2` / `LLaMA2` supported.
- **Basic model build and prediction**.
```py
!pip install sentencepiece # required for llama2 tokenizer
os.environ['KECAM_BACKEND'] = 'keras_core'
os.environ['KERAS_BACKEND'] = 'jax'
import kecam
print(f"{kecam.backend.backend() = }")
# kecam.backend.backend() = 'jax'
mm = kecam.llama2.LLaMA2_42M()
# >>>> Load pretrained from: ~/.keras/models/llama2_42m_tiny_stories.h5
mm.run_prediction('As evening fell, a maiden stood at the edge of a wood. In her hands,')
# >>>> Load tokenizer from file: ~/.keras/datasets/llama_tokenizer.model
#
# As evening fell, a maiden stood at the edge of a wood. In her hands, she held a beautiful diamond. Everyone was surprised to see it.
# "What is it?" one of the kids asked.
# "It's a diamond," the maiden said.
# ...
```
***
# Recognition Models
## AotNet
- [Keras AotNet](keras_cv_attention_models/aotnet) is just a `ResNet` / `ResNetV2` like framework, that set parameters like `attn_types` and `se_ratio` and others, which is used to apply different types attention layer. Works like `byoanet` / `byobnet` from `timm`.
- Default parameters set is a typical `ResNet` architecture with `Conv2D use_bias=False` and `padding` like `PyTorch`.
```py
from keras_cv_attention_models import aotnet
# Mixing se and outlook and halo and mhsa and cot_attention, 21M parameters.
# 50 is just a picked number that larger than the relative `num_block`.
attn_types = [None, "outlook", ["bot", "halo"] * 50, "cot"],
se_ratio = [0.25, 0, 0, 0],
model = aotnet.AotNet50V2(attn_types=attn_types, se_ratio=se_ratio, stem_type="deep", strides=1)
model.summary()
```
## BEiT
- [Keras BEiT](keras_cv_attention_models/beit) includes models from [PDF 2106.08254 BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/pdf/2106.08254.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------- | ------- | ------- | ----- | -------- | ------------ |
| [BeitBasePatch16, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_base_patch16_224_imagenet21k-ft1k.h5) | 86.53M | 17.61G | 224 | 85.240 | 321.226 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_base_patch16_384_imagenet21k-ft1k.h5) | 86.74M | 55.70G | 384 | 86.808 | 164.705 qps |
| [BeitLargePatch16, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_large_patch16_224_imagenet21k-ft1k.h5) | 304.43M | 61.68G | 224 | 87.476 | 105.998 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_large_patch16_384_imagenet21k-ft1k.h5) | 305.00M | 191.65G | 384 | 88.382 | 45.7307 qps |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_large_patch16_512_imagenet21k-ft1k.h5) | 305.67M | 363.46G | 512 | 88.584 | 21.3097 qps |
## BEiTV2
- [Keras BEiT](keras_cv_attention_models/beit) includes models from BeitV2 Paper [PDF 2208.06366 BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://arxiv.org/pdf/2208.06366.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------- | ------ | ----- | -------- | ------------ |
| BeitV2BasePatch16 | 86.53M | 17.61G | 224 | 85.5 | 322.52 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_v2_base_patch16_224_imagenet21k-ft1k.h5) | 86.53M | 17.61G | 224 | 86.5 | 322.52 qps |
| BeitV2LargePatch16 | 304.43M | 61.68G | 224 | 87.3 | 105.734 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/beit_v2_large_patch16_224_imagenet21k-ft1k.h5) | 304.43M | 61.68G | 224 | 88.4 | 105.734 qps |
## BotNet
- [Keras BotNet](keras_cv_attention_models/botnet) is for [PDF 2101.11605 Bottleneck Transformers for Visual Recognition](https://arxiv.org/pdf/2101.11605.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ------ | ----- | -------- | ------------ |
| BotNet50 | 21M | 5.42G | 224 | | 746.454 qps |
| BotNet101 | 41M | 9.13G | 224 | | 448.102 qps |
| BotNet152 | 56M | 12.84G | 224 | | 316.671 qps |
| [BotNet26T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/botnet/botnet26t_256_imagenet.h5) | 12.5M | 3.30G | 256 | 79.246 | 1188.84 qps |
| [BotNextECA26T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/botnet/botnext_eca26t_256_imagenet.h5) | 10.59M | 2.45G | 256 | 79.270 | 1038.19 qps |
| [BotNetSE33T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/botnet/botnet_se33t_256_imagenet.h5) | 13.7M | 3.89G | 256 | 81.2 | 610.429 qps |
## CAFormer
- [Keras CAFormer](keras_cv_attention_models/caformer) is for [PDF 2210.13452 MetaFormer Baselines for Vision](https://arxiv.org/pdf/2210.13452.pdf). `CAFormer` is using 2 transformer stacks, while `ConvFormer` is all conv blocks.
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------------- | ------ | ----- | ----- | -------- | ------------ |
| [CAFormerS18](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s18_224_imagenet.h5) | 26M | 4.1G | 224 | 83.6 | 399.127 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s18_384_imagenet.h5) | 26M | 13.4G | 384 | 85.0 | 181.993 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s18_224_imagenet21k-ft1k.h5) | 26M | 4.1G | 224 | 84.1 | 399.127 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s18_384_imagenet21k-ft1k.h5) | 26M | 13.4G | 384 | 85.4 | 181.993 qps |
| [CAFormerS36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s36_224_imagenet.h5) | 39M | 8.0G | 224 | 84.5 | 204.328 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s36_384_imagenet.h5) | 39M | 26.0G | 384 | 85.7 | 102.04 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s36_224_imagenet21k-ft1k.h5) | 39M | 8.0G | 224 | 85.8 | 204.328 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_s36_384_imagenet21k-ft1k.h5) | 39M | 26.0G | 384 | 86.9 | 102.04 qps |
| [CAFormerM36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_m36_224_imagenet.h5) | 56M | 13.2G | 224 | 85.2 | 162.257 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_m36_384_imagenet.h5) | 56M | 42.0G | 384 | 86.2 | 65.6188 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_m36_224_imagenet21k-ft1k.h5) | 56M | 13.2G | 224 | 86.6 | 162.257 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_m36_384_imagenet21k-ft1k.h5) | 56M | 42.0G | 384 | 87.5 | 65.6188 qps |
| [CAFormerB36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_b36_224_imagenet.h5) | 99M | 23.2G | 224 | 85.5 | 116.865 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_b36_384_imagenet.h5) | 99M | 72.2G | 384 | 86.4 | 50.0244 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_b36_224_imagenet21k-ft1k.h5) | 99M | 23.2G | 224 | 87.4 | 116.865 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/caformer_b36_384_imagenet21k-ft1k.h5) | 99M | 72.2G | 384 | 88.1 | 50.0244 qps |
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------------- | ------ | ----- | ----- | -------- | ------------ |
| [ConvFormerS18](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s18_224_imagenet.h5) | 27M | 3.9G | 224 | 83.0 | 295.114 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s18_384_imagenet.h5) | 27M | 11.6G | 384 | 84.4 | 145.923 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s18_224_imagenet21k-ft1k.h5) | 27M | 3.9G | 224 | 83.7 | 295.114 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s36_384_imagenet21k-ft1k.h5) | 27M | 11.6G | 384 | 85.0 | 145.923 qps |
| [ConvFormerS36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s36_224_imagenet.h5) | 40M | 7.6G | 224 | 84.1 | 161.609 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s36_384_imagenet.h5) | 40M | 22.4G | 384 | 85.4 | 80.2101 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s36_224_imagenet21k-ft1k.h5) | 40M | 7.6G | 224 | 85.4 | 161.609 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_s36_384_imagenet21k-ft1k.h5) | 40M | 22.4G | 384 | 86.4 | 80.2101 qps |
| [ConvFormerM36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_m36_224_imagenet.h5) | 57M | 12.8G | 224 | 84.5 | 130.161 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_m36_384_imagenet.h5) | 57M | 37.7G | 384 | 85.6 | 63.9712 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_m36_224_imagenet21k-ft1k.h5) | 57M | 12.8G | 224 | 86.1 | 130.161 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_m36_384_imagenet21k-ft1k.h5) | 57M | 37.7G | 384 | 86.9 | 63.9712 qps |
| [ConvFormerB36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_b36_224_imagenet.h5) | 100M | 22.6G | 224 | 84.8 | 98.0751 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_b36_384_imagenet.h5) | 100M | 66.5G | 384 | 85.7 | 48.5897 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_b36_224_imagenet21k-ft1k.h5) | 100M | 22.6G | 224 | 87.0 | 98.0751 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/caformer/convformer_b36_384_imagenet21k-ft1k.h5) | 100M | 66.5G | 384 | 87.6 | 48.5897 qps |
## CMT
- [Keras CMT](keras_cv_attention_models/cmt) is for [PDF 2107.06263 CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/pdf/2107.06263.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------------------------------- | ------ | ----- | ----- | -------- | ------------ |
| CMTTiny, (Self trained 105 epochs) | 9.5M | 0.65G | 160 | 77.4 | 315.566 qps |
| - [(305 epochs)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_tiny_160_imagenet.h5) | 9.5M | 0.65G | 160 | 78.94 | 315.566 qps |
| - [224, (fine-tuned 69 epochs)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_tiny_224_imagenet.h5) | 9.5M | 1.32G | 224 | 80.73 | 254.87 qps |
| [CMTTiny_torch, (1000 epochs)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_tiny_torch_160_imagenet.h5) | 9.5M | 0.65G | 160 | 79.2 | 338.207 qps |
| [CMTXS_torch](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_xs_torch_192_imagenet.h5) | 15.2M | 1.58G | 192 | 81.8 | 241.288 qps |
| [CMTSmall_torch](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_small_torch_224_imagenet.h5) | 25.1M | 4.09G | 224 | 83.5 | 171.109 qps |
| [CMTBase_torch](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cmt/cmt_base_torch_256_imagenet.h5) | 45.7M | 9.42G | 256 | 84.5 | 103.34 qps |
## CoaT
- [Keras CoaT](keras_cv_attention_models/coat) is for [PDF 2104.06399 CoaT: Co-Scale Conv-Attentional Image Transformers](http://arxiv.org/abs/2104.06399).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ----- | ----- | -------- | ------------ |
| [CoaTLiteTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coat/coat_lite_tiny_imagenet.h5) | 5.7M | 1.60G | 224 | 77.5 | 450.27 qps |
| [CoaTLiteMini](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coat/coat_lite_mini_imagenet.h5) | 11M | 2.00G | 224 | 79.1 | 452.884 qps |
| [CoaTLiteSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coat/coat_lite_small_imagenet.h5) | 20M | 3.97G | 224 | 81.9 | 248.846 qps |
| [CoaTTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coat/coat_tiny_imagenet.h5) | 5.5M | 4.33G | 224 | 78.3 | 152.495 qps |
| [CoaTMini](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coat/coat_mini_imagenet.h5) | 10M | 6.78G | 224 | 81.0 | 124.845 qps |
## CoAtNet
- [Keras CoAtNet](keras_cv_attention_models/coatnet) is for [PDF 2106.04803 CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://arxiv.org/pdf/2106.04803.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [CoAtNet0, 160, (105 epochs)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coatnet/coatnet0_160_imagenet.h5) | 23.3M | 2.09G | 160 | 80.48 | 584.059 qps |
| [CoAtNet0, (305 epochs)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/coatnet/coatnet0_224_imagenet.h5) | 23.8M | 4.22G | 224 | 82.79 | 400.333 qps |
| CoAtNet0 | 25M | 4.6G | 224 | 82.0 | 400.333 qps |
| - use_dw_strides=False | 25M | 4.2G | 224 | 81.6 | 461.197 qps |
| CoAtNet1 | 42M | 8.8G | 224 | 83.5 | 206.954 qps |
| - use_dw_strides=False | 42M | 8.4G | 224 | 83.3 | 228.938 qps |
| CoAtNet2 | 75M | 16.6G | 224 | 84.1 | 156.359 qps |
| - use_dw_strides=False | 75M | 15.7G | 224 | 84.1 | 165.846 qps |
| CoAtNet2, 21k_ft1k | 75M | 16.6G | 224 | 87.1 | 156.359 qps |
| CoAtNet3 | 168M | 34.7G | 224 | 84.5 | 95.0703 qps |
| CoAtNet3, 21k_ft1k | 168M | 34.7G | 224 | 87.6 | 95.0703 qps |
| CoAtNet3, 21k_ft1k | 168M | 203.1G | 512 | 87.9 | 95.0703 qps |
| CoAtNet4, 21k_ft1k | 275M | 360.9G | 512 | 88.1 | 74.6022 qps |
| CoAtNet4, 21k_ft1k, PT-RA-E150 | 275M | 360.9G | 512 | 88.56 | 74.6022 qps |
## ConvNeXt
- [Keras ConvNeXt](keras_cv_attention_models/convnext) is for [PDF 2201.03545 A ConvNet for the 2020s](https://arxiv.org/pdf/2201.03545.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------------- | ------ | ------- | ----- | -------- | ------------ |
| [ConvNeXtTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_tiny_imagenet.h5) | 28M | 4.49G | 224 | 82.1 | 361.58 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_tiny_224_imagenet21k-ft1k.h5) | 28M | 4.49G | 224 | 82.9 | 361.58 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_tiny_384_imagenet21k-ft1k.h5) | 28M | 13.19G | 384 | 84.1 | 182.134 qps |
| [ConvNeXtSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_small_imagenet.h5) | 50M | 8.73G | 224 | 83.1 | 202.007 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_small_224_imagenet21k-ft1k.h5) | 50M | 8.73G | 224 | 84.6 | 202.007 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_small_384_imagenet21k-ft1k.h5) | 50M | 25.67G | 384 | 85.8 | 108.125 qps |
| [ConvNeXtBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_base_224_imagenet.h5) | 89M | 15.42G | 224 | 83.8 | 160.036 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_base_384_imagenet.h5) | 89M | 45.32G | 384 | 85.1 | 83.3095 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_base_224_imagenet21k-ft1k.h5) | 89M | 15.42G | 224 | 85.8 | 160.036 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_base_384_imagenet21k-ft1k.h5) | 89M | 45.32G | 384 | 86.8 | 83.3095 qps |
| [ConvNeXtLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_large_224_imagenet.h5) | 198M | 34.46G | 224 | 84.3 | 102.27 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_large_384_imagenet.h5) | 198M | 101.28G | 384 | 85.5 | 47.2086 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_large_224_imagenet21k-ft1k.h5) | 198M | 34.46G | 224 | 86.6 | 102.27 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_large_384_imagenet21k-ft1k.h5) | 198M | 101.28G | 384 | 87.5 | 47.2086 qps |
| [ConvNeXtXlarge, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_xlarge_224_imagenet21k-ft1k.h5) | 350M | 61.06G | 224 | 87.0 | 40.5776 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_xlarge_384_imagenet21k-ft1k.h5) | 350M | 179.43G | 384 | 87.8 | 21.797 qps |
| [ConvNeXtXXLarge, clip](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_xxlarge_clip-ft1k.h5) | 846M | 198.09G | 256 | 88.6 | |
## ConvNeXtV2
- [Keras ConvNeXt](keras_cv_attention_models/convnext) includes implementation of [PDF 2301.00808 ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/pdf/2301.00808.pdf). **Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only**.
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------------- | ------ | ------ | ----- | -------- | ------------ |
| [ConvNeXtV2Atto](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_atto_imagenet.h5) | 3.7M | 0.55G | 224 | 76.7 | 705.822 qps |
| [ConvNeXtV2Femto](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_femto_imagenet.h5) | 5.2M | 0.78G | 224 | 78.5 | 728.02 qps |
| [ConvNeXtV2Pico](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_pico_imagenet.h5) | 9.1M | 1.37G | 224 | 80.3 | 591.502 qps |
| [ConvNeXtV2Nano](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_nano_imagenet.h5) | 15.6M | 2.45G | 224 | 81.9 | 471.918 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_nano_224_imagenet21k-ft1k.h5) | 15.6M | 2.45G | 224 | 82.1 | 471.918 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_nano_384_imagenet21k-ft1k.h5) | 15.6M | 7.21G | 384 | 83.4 | 213.802 qps |
| [ConvNeXtV2Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_tiny_imagenet.h5) | 28.6M | 4.47G | 224 | 83.0 | 301.982 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_tiny_224_imagenet21k-ft1k.h5) | 28.6M | 4.47G | 224 | 83.9 | 301.982 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_tiny_384_imagenet21k-ft1k.h5) | 28.6M | 13.1G | 384 | 85.1 | 139.578 qps |
| [ConvNeXtV2Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_base_imagenet.h5) | 89M | 15.4G | 224 | 84.9 | 132.575 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_base_224_imagenet21k-ft1k.h5) | 89M | 15.4G | 224 | 86.8 | 132.575 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_base_384_imagenet21k-ft1k.h5) | 89M | 45.2G | 384 | 87.7 | 66.5729 qps |
| [ConvNeXtV2Large](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_large_imagenet.h5) | 198M | 34.4G | 224 | 85.8 | 86.8846 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_large_224_imagenet21k-ft1k.h5) | 198M | 34.4G | 224 | 87.3 | 86.8846 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_large_384_imagenet21k-ft1k.h5) | 198M | 101.1G | 384 | 88.2 | 24.4542 qps |
| [ConvNeXtV2Huge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_huge_imagenet.h5) | 660M | 115G | 224 | 86.3 | |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_huge_384_imagenet21k-ft1k.h5) | 660M | 337.9G | 384 | 88.7 | |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/convnext/convnext_v2_huge_512_imagenet21k-ft1k.h5) | 660M | 600.8G | 512 | 88.9 | |
## CoTNet
- [Keras CoTNet](keras_cv_attention_models/cotnet) is for [PDF 2107.12292 Contextual Transformer Networks for Visual Recognition](https://arxiv.org/pdf/2107.12292.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------ |:------:| ------ | ----- |:--------:| ------------ |
| [CotNet50](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet50_224_imagenet.h5) | 22.2M | 3.25G | 224 | 81.3 | 324.913 qps |
| [CotNetSE50D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet_se50d_224_imagenet.h5) | 23.1M | 4.05G | 224 | 81.6 | 513.077 qps |
| [CotNet101](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet101_224_imagenet.h5) | 38.3M | 6.07G | 224 | 82.8 | 183.824 qps |
| [CotNetSE101D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet_se101d_224_imagenet.h5) | 40.9M | 8.44G | 224 | 83.2 | 251.487 qps |
| [CotNetSE152D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet_se152d_224_imagenet.h5) | 55.8M | 12.22G | 224 | 84.0 | 175.469 qps |
| [CotNetSE152D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cotnet/cotnet_se152d_320_imagenet.h5) | 55.8M | 24.92G | 320 | 84.6 | 175.469 qps |
## CSPNeXt
- [Keras CSPNeXt](keras_cv_attention_models/cspnext) is for backbone of [PDF 2212.07784 RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://arxiv.org/abs/2212.07784).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ----- | ----- | -------- | -------- |
| [CSPNeXtTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cspnext/cspnext_tiny_imagenet.h5) | 2.73M | 0.34G | 224 | 69.44 | |
| [CSPNeXtSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cspnext/cspnext_small_imagenet.h5) | 4.89M | 0.66G | 224 | 74.41 | |
| [CSPNeXtMedium](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cspnext/cspnext_medium_imagenet.h5) | 13.05M | 1.92G | 224 | 79.27 | |
| [CSPNeXtLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cspnext/cspnext_large_imagenet.h5) | 27.16M | 4.19G | 224 | 81.30 | |
| [CSPNeXtXLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/cspnext/cspnext_xlarge_imagenet.h5) | 48.85M | 7.75G | 224 | 82.10 | |
## DaViT
- [Keras DaViT](keras_cv_attention_models/davit) is for [PDF 2204.03645 DaViT: Dual Attention Vision Transformers](https://arxiv.org/pdf/2204.03645.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------ | ------ | ----- | -------- | ------------ |
| [DaViT_T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/davit/davit_t_imagenet.h5) | 28.36M | 4.56G | 224 | 82.8 | 224.563 qps |
| [DaViT_S](https://github.com/leondgarse/keras_cv_attention_models/releases/download/davit/davit_s_imagenet.h5) | 49.75M | 8.83G | 224 | 84.2 | 145.838 qps |
| [DaViT_B](https://github.com/leondgarse/keras_cv_attention_models/releases/download/davit/davit_b_imagenet.h5) | 87.95M | 15.55G | 224 | 84.6 | 114.527 qps |
| DaViT_L, 21k_ft1k | 196.8M | 103.2G | 384 | 87.5 | 34.7015 qps |
| DaViT_H, 1.5B | 348.9M | 327.3G | 512 | 90.2 | 12.363 qps |
| DaViT_G, 1.5B | 1.406B | 1.022T | 512 | 90.4 | |
## DiNAT
- [Keras DiNAT](keras_cv_attention_models/nat) is for [PDF 2209.15001 Dilated Neighborhood Attention Transformer](https://arxiv.org/pdf/2209.15001.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [DiNAT_Mini](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_mini_imagenet.h5) | 20.0M | 2.73G | 224 | 81.8 | 83.9943 qps |
| [DiNAT_Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_tiny_imagenet.h5) | 27.9M | 4.34G | 224 | 82.7 | 61.1902 qps |
| [DiNAT_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_small_imagenet.h5) | 50.7M | 7.84G | 224 | 83.8 | 41.0343 qps |
| [DiNAT_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_base_imagenet.h5) | 89.8M | 13.76G | 224 | 84.4 | 30.1332 qps |
| [DiNAT_Large, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_large_224_imagenet21k-ft1k.h5) | 200.9M | 30.58G | 224 | 86.6 | 18.4936 qps |
| - [21k, (num_classes=21841)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_large_imagenet21k.h5) | 200.9M | 30.58G | 224 | | |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_large_384_imagenet21k-ft1k.h5) | 200.9M | 89.86G | 384 | 87.4 | |
| [DiNAT_Large_K11, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/dinat_large_k11_imagenet21k-ft1k.h5) | 201.1M | 92.57G | 384 | 87.5 | |
## DINOv2
- [Keras DINOv2](keras_cv_attention_models/beit) includes models from [PDF 2304.07193 DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/pdf/2304.07193.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------- | ------- | ----- | -------- | ------------ |
| [DINOv2_ViT_Small14](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/dinov2_vit_small14_518_imagenet.h5) | 22.83M | 47.23G | 518 | 81.1 | 165.271 qps |
| [DINOv2_ViT_Base14](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/dinov2_vit_base14_518_imagenet.h5) | 88.12M | 152.6G | 518 | 84.5 | 54.9769 qps |
| [DINOv2_ViT_Large14](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/dinov2_vit_large14_518_imagenet.h5) | 306.4M | 509.6G | 518 | 86.3 | 17.4108 qps |
| [DINOv2_ViT_Giant14](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/dinov2_vit_giant14_518_imagenet.h5) | 1139.6M | 1790.3G | 518 | 86.5 | |
## EdgeNeXt
- [Keras EdgeNeXt](keras_cv_attention_models/edgenext) is for [PDF 2206.10589 EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications](https://arxiv.org/pdf/2206.10589.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------------- | ------ | ------ | ----- | -------- | ------------ |
| [EdgeNeXt_XX_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_xx_small_256_imagenet.h5) | 1.33M | 266M | 256 | 71.23 | 902.957 qps |
| [EdgeNeXt_X_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_x_small_256_imagenet.h5) | 2.34M | 547M | 256 | 74.96 | 638.346 qps |
| [EdgeNeXt_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_small_256_imagenet.h5) | 5.59M | 1.27G | 256 | 79.41 | 536.762 qps |
| - [usi](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_small_256_usi.h5) | 5.59M | 1.27G | 256 | 81.07 | 536.762 qps |
| [EdgeNeXt_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_base_256_imagenet.h5) | 18.5M | 3.86G | 256 | 82.47 | 383.461 qps |
| - [usi](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_base_256_usi.h5) | 18.5M | 3.86G | 256 | 83.31 | 383.461 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/edgenext/edgenext_base_256_imagenet-ft1k.h5) | 18.5M | 3.86G | 256 | 83.68 | 383.461 qps |
## EfficientFormer
- [Keras EfficientFormer](keras_cv_attention_models/efficientformer) is for [PDF 2206.01191 EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/pdf/2206.01191.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------- | ------ | ----- | ----- | -------- | ------------ |
| [EfficientFormerL1, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/efficientformer_l1_224_imagenet.h5) | 12.3M | 1.31G | 224 | 79.2 | 1214.22 qps |
| [EfficientFormerL3, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/efficientformer_l3_224_imagenet.h5) | 31.4M | 3.95G | 224 | 82.4 | 596.705 qps |
| [EfficientFormerL7, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/efficientformer_l7_224_imagenet.h5) | 74.4M | 9.79G | 224 | 83.3 | 298.434 qps |
## EfficientFormerV2
- [Keras EfficientFormer](keras_cv_attention_models/efficientformer) includes implementation of [PDF 2212.08059 Rethinking Vision Transformers for MobileNet Size and Speed](https://arxiv.org/pdf/2212.08059.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [EfficientFormerV2S0, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientformer/efficientformer_v2_s0_224_imagenet.h5) | 3.60M | 405.2M | 224 | 76.2 | 1114.38 qps |
| [EfficientFormerV2S1, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientformer/efficientformer_v2_s1_224_imagenet.h5) | 6.19M | 665.6M | 224 | 79.7 | 841.186 qps |
| [EfficientFormerV2S2, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientformer/efficientformer_v2_s2_224_imagenet.h5) | 12.7M | 1.27G | 224 | 82.0 | 573.9 qps |
| [EfficientFormerV2L, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientformer/efficientformer_v2_l_224_imagenet.h5) | 26.3M | 2.59G | 224 | 83.5 | 377.224 qps |
## EfficientNet
- [Keras EfficientNet](keras_cv_attention_models/efficientnet) includes implementation of [PDF 1911.04252 Self-training with Noisy Student improves ImageNet classification](https://arxiv.org/pdf/1911.04252.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------------ | ------ | ------- | ----- | -------- | ------------ |
| [EfficientNetV1B0](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b0-imagenet.h5) | 5.3M | 0.39G | 224 | 77.6 | 1129.93 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b0-noisy_student.h5) | 5.3M | 0.39G | 224 | 78.8 | 1129.93 qps |
| [EfficientNetV1B1](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b1-imagenet.h5) | 7.8M | 0.70G | 240 | 79.6 | 758.639 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b1-noisy_student.h5) | 7.8M | 0.70G | 240 | 81.5 | 758.639 qps |
| [EfficientNetV1B2](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b2-imagenet.h5) | 9.1M | 1.01G | 260 | 80.5 | 668.959 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b2-noisy_student.h5) | 9.1M | 1.01G | 260 | 82.4 | 668.959 qps |
| [EfficientNetV1B3](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b3-imagenet.h5) | 12.2M | 1.86G | 300 | 81.9 | 473.607 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b3-noisy_student.h5) | 12.2M | 1.86G | 300 | 84.1 | 473.607 qps |
| [EfficientNetV1B4](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b4-imagenet.h5) | 19.3M | 4.46G | 380 | 83.3 | 265.244 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b4-noisy_student.h5) | 19.3M | 4.46G | 380 | 85.3 | 265.244 qps |
| [EfficientNetV1B5](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b5-imagenet.h5) | 30.4M | 10.40G | 456 | 84.3 | 146.758 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b5-noisy_student.h5) | 30.4M | 10.40G | 456 | 86.1 | 146.758 qps |
| [EfficientNetV1B6](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b6-imagenet.h5) | 43.0M | 19.29G | 528 | 84.8 | 88.0369 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b6-noisy_student.h5) | 43.0M | 19.29G | 528 | 86.4 | 88.0369 qps |
| [EfficientNetV1B7](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b7-imagenet.h5) | 66.3M | 38.13G | 600 | 85.2 | 52.6616 qps |
| - [NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-b7-noisy_student.h5) | 66.3M | 38.13G | 600 | 86.9 | 52.6616 qps |
| [EfficientNetV1L2, NoisyStudent](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetv1-l2-noisy_student.h5) | 480.3M | 477.98G | 800 | 88.4 | |
## EfficientNetEdgeTPU
- [Keras EfficientNetEdgeTPU](keras_cv_attention_models/efficientnet) includes implementation of [PDF 1911.04252 Self-training with Noisy Student improves ImageNet classification](https://arxiv.org/pdf/1911.04252.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------------ | ------ | ------- | ----- | -------- | ------------ |
| [EfficientNetEdgeTPUSmall](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetedgetpu-small-imagenet.h5) | 5.49M | 1.79G | 224 | 78.07 | 1459.38 qps |
| [EfficientNetEdgeTPUMedium](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetedgetpu-medium-imagenet.h5) | 6.90M | 3.01G | 240 | 79.25 | 1028.95 qps |
| [EfficientNetEdgeTPULarge](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv1_pretrained/efficientnetedgetpu-large-imagenet.h5) | 10.59M | 7.94G | 300 | 81.32 | 527.034 qps |
## EfficientNetV2
- [Keras EfficientNet](keras_cv_attention_models/efficientnet) includes implementation of [PDF 2104.00298 EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [EfficientNetV2B0](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b0-imagenet.h5) | 7.1M | 0.72G | 224 | 78.7 | 1109.84 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b0-21k-ft1k.h5) | 7.1M | 0.72G | 224 | 77.55? | 1109.84 qps |
| [EfficientNetV2B1](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b1-imagenet.h5) | 8.1M | 1.21G | 240 | 79.8 | 842.372 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b1-21k-ft1k.h5) | 8.1M | 1.21G | 240 | 79.03? | 842.372 qps |
| [EfficientNetV2B2](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b2-imagenet.h5) | 10.1M | 1.71G | 260 | 80.5 | 762.865 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b2-21k-ft1k.h5) | 10.1M | 1.71G | 260 | 79.48? | 762.865 qps |
| [EfficientNetV2B3](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b3-imagenet.h5) | 14.4M | 3.03G | 300 | 82.1 | 548.501 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-b3-21k-ft1k.h5) | 14.4M | 3.03G | 300 | 82.46? | 548.501 qps |
| [EfficientNetV2T](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-t-imagenet.h5) | 13.6M | 3.18G | 288 | 82.34 | 496.483 qps |
| [EfficientNetV2T_GC](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-t-gc-imagenet.h5) | 13.7M | 3.19G | 288 | 82.46 | 368.763 qps |
| [EfficientNetV2S](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-s-imagenet.h5) | 21.5M | 8.41G | 384 | 83.9 | 344.109 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-s-21k-ft1k.h5) | 21.5M | 8.41G | 384 | 84.9 | 344.109 qps |
| [EfficientNetV2M](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-m-imagenet.h5) | 54.1M | 24.69G | 480 | 85.2 | 145.346 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-m-21k-ft1k.h5) | 54.1M | 24.69G | 480 | 86.2 | 145.346 qps |
| [EfficientNetV2L](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-l-imagenet.h5) | 119.5M | 56.27G | 480 | 85.7 | 85.6514 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-l-21k-ft1k.h5) | 119.5M | 56.27G | 480 | 86.9 | 85.6514 qps |
| [EfficientNetV2XL, 21k_ft1k](https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-xl-21k-ft1k.h5) | 206.8M | 93.66G | 512 | 87.2 | 55.141 qps |
## EfficientViT_B
- [Keras EfficientViT_B](keras_cv_attention_models/efficientvit) is for Paper [PDF 2205.14756 EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation](https://arxiv.org/pdf/2205.14756.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------------- | ------ | ----- | ----- | -------- | ------------ |
| [EfficientViT_B0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b0_224_imagenet.h5) | 3.41M | 0.12G | 224 | 71.6 ? | 1581.76 qps |
| [EfficientViT_B1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b1_224_imagenet.h5) | 9.10M | 0.58G | 224 | 79.4 | 943.587 qps |
| - [256](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b1_256_imagenet.h5) | 9.10M | 0.78G | 256 | 79.9 | 840.844 qps |
| - [288](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b1_288_imagenet.h5) | 9.10M | 1.03G | 288 | 80.4 | 680.088 qps |
| [EfficientViT_B2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b2_224_imagenet.h5) | 24.33M | 1.68G | 224 | 82.1 | 583.295 qps |
| - [256](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b2_256_imagenet.h5) | 24.33M | 2.25G | 256 | 82.7 | 507.187 qps |
| - [288](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b2_288_imagenet.h5) | 24.33M | 2.92G | 288 | 83.1 | 419.93 qps |
| [EfficientViT_B3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b3_224_imagenet.h5) | 48.65M | 4.14G | 224 | 83.5 | 329.764 qps |
| - [256](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b3_256_imagenet.h5) | 48.65M | 5.51G | 256 | 83.8 | 288.605 qps |
| - [288](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_b3_288_imagenet.h5) | 48.65M | 7.14G | 288 | 84.2 | 229.992 qps |
| [EfficientViT_L1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_l1_224_imagenet.h5) | 52.65M | 5.28G | 224 | 84.48 | 503.068 qps |
| [EfficientViT_L2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_l2_224_imagenet.h5) | 63.71M | 6.98G | 224 | 85.05 | 396.255 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_l2_384_imagenet.h5) | 63.71M | 20.7G | 384 | 85.98 | 207.322 qps |
| [EfficientViT_L3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_l3_224_imagenet.h5) | 246.0M | 27.6G | 224 | 85.814 | 174.926 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_l3_384_imagenet.h5) | 246.0M | 81.6G | 384 | 86.408 | 86.895 qps |
## EfficientViT_M
- [Keras EfficientViT_M](keras_cv_attention_models/efficientvit) is for Paper [PDF 2305.07027 EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention](https://arxiv.org/pdf/2305.07027.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------------- | ------ | ----- | ----- | -------- | ------------ |
| [EfficientViT_M0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m0_224_imagenet.h5) | 2.35M | 79.4M | 224 | 63.2 | 814.522 qps |
| [EfficientViT_M1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m1_224_imagenet.h5) | 2.98M | 167M | 224 | 68.4 | 948.041 qps |
| [EfficientViT_M2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m2_224_imagenet.h5) | 4.19M | 201M | 224 | 70.8 | 906.286 qps |
| [EfficientViT_M3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m3_224_imagenet.h5) | 6.90M | 263M | 224 | 73.4 | 758.086 qps |
| [EfficientViT_M4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m4_224_imagenet.h5) | 8.80M | 299M | 224 | 74.3 | 672.891 qps |
| [EfficientViT_M5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientvit/efficientvit_m5_224_imagenet.h5) | 12.47M | 522M | 224 | 77.1 | 577.254 qps |
## EVA
- [Keras EVA](keras_cv_attention_models/beit) includes models from [PDF 2211.07636 EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://arxiv.org/pdf/2211.07636.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------------------- | ------- | -------- | ----- | -------- | ------------ |
| [EvaLargePatch14, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva_large_patch14_196_imagenet21k-ft1k.h5) | 304.14M | 61.65G | 196 | 88.59 | 115.532 qps |
| - [21k_ft1k, 336](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva_large_patch14_336_imagenet21k-ft1k.h5) | 304.53M | 191.55G | 336 | 89.20 | 53.3467 qps |
| [EvaGiantPatch14, clip](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva_giant_patch14_224_imagenet21k-ft1k.h5) | 1012.6M | 267.40G | 224 | 89.10 | |
| - [m30m](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva_giant_patch14_336_imagenet21k-ft1k.h5) | 1013.0M | 621.45G | 336 | 89.57 | |
| - [m30m](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva_giant_patch14_560_imagenet21k-ft1k.h5) | 1014.4M | 1911.61G | 560 | 89.80 | |
## EVA02
- [Keras EVA02](keras_cv_attention_models/beit) includes models from [PDF 2303.11331 EVA: EVA-02: A Visual Representation for Neon Genesis](https://arxiv.org/pdf/2303.11331.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------------------- | ------- | ------- | ----- | -------- | ------------ |
| [EVA02TinyPatch14, mim_in22k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva02_tiny_patch14_336_mim_in22k_ft1k.h5) | 5.76M | 4.72G | 336 | 80.658 | 320.123 qps |
| [EVA02SmallPatch14, mim_in22k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva02_small_patch14_336_mim_in22k_ft1k.h5) | 22.13M | 15.57G | 336 | 85.74 | 161.774 qps |
| [EVA02BasePatch14, mim_in22k_ft22k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva02_base_patch14_448_mim_in22k_ft22k_ft1k.h5) | 87.12M | 107.6G | 448 | 88.692 | 34.3962 qps |
| [EVA02LargePatch14, mim_m38m_ft22k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/eva02_large_patch14_448_mim_m38m_ft22k_ft1k.h5) | 305.08M | 363.68G | 448 | 90.054 | |
## FasterNet
- [Keras FasterNet](keras_cv_attention_models/fasternet) includes implementation of [PDF 2303.03667 Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks ](https://arxiv.org/pdf/2303.03667.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ----------- | ------ | ------ | ----- | -------- | ------------ |
| [FasterNetT0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_t0_imagenet.h5) | 3.9M | 0.34G | 224 | 71.9 | 1890.83 qps |
| [FasterNetT1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_t1_imagenet.h5) | 7.6M | 0.85G | 224 | 76.2 | 1788.16 qps |
| [FasterNetT2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_t2_imagenet.h5) | 15.0M | 1.90G | 224 | 78.9 | 1353.12 qps |
| [FasterNetS](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_s_imagenet.h5) | 31.1M | 4.55G | 224 | 81.3 | 818.814 qps |
| [FasterNetM](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_m_imagenet.h5) | 53.5M | 8.72G | 224 | 83.0 | 436.383 qps |
| [FasterNetL](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fasternet/fasternet_l_imagenet.h5) | 93.4M | 15.49G | 224 | 83.5 | 319.809 qps |
## FasterViT
- [Keras FasterViT](keras_cv_attention_models/fastervit) includes implementation of [PDF 2306.06189 FasterViT: Fast Vision Transformers with Hierarchical Attention](https://arxiv.org/pdf/2306.06189.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------- | -------- | ------- | ----- | -------- | ------------ |
| [FasterViT0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_0_224_imagenet.h5) | 31.40M | 3.51G | 224 | 82.1 | 716.809 qps |
| [FasterViT1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_1_224_imagenet.h5) | 53.37M | 5.52G | 224 | 83.2 | 491.971 qps |
| [FasterViT2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_2_224_imagenet.h5) | 75.92M | 9.00G | 224 | 84.2 | 377.006 qps |
| [FasterViT3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_3_224_imagenet.h5) | 159.55M | 18.75G | 224 | 84.9 | 216.481 qps |
| [FasterViT4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_4_224_imagenet.h5) | 351.12M | 41.57G | 224 | 85.4 | 71.6303 qps |
| [FasterViT5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_5_224_imagenet.h5) | 957.52M | 114.08G | 224 | 85.6 | |
| [FasterViT6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_6_224_imagenet.1.h5), [+.2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastervit/fastervit_6_224_imagenet.2.h5) | 1360.33M | 144.13G | 224 | 85.8 | |
## FastViT
- [Keras FastViT](keras_cv_attention_models/fastvit) includes implementation of [PDF 2303.14189 FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://arxiv.org/pdf/2303.14189.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ----- | ----- | -------- | ------------ |
| [FastViT_T8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_t8_imagenet.h5) | 4.03M | 0.65G | 256 | 76.2 | 1020.29 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_t8_distill.h5) | 4.03M | 0.65G | 256 | 77.2 | 1020.29 qps |
| - deploy=True | 3.99M | 0.64G | 256 | 76.2 | 1323.14 qps |
| [FastViT_T12](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_t12_imagenet.h5) | 7.55M | 1.34G | 256 | 79.3 | 734.867 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_t12_distill.h5) | 7.55M | 1.34G | 256 | 80.3 | 734.867 qps |
| - deploy=True | 7.50M | 1.33G | 256 | 79.3 | 956.332 qps |
| [FastViT_S12](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_s12_imagenet.h5) | 9.47M | 1.74G | 256 | 79.9 | 666.669 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_s12_distill.h5) | 9.47M | 1.74G | 256 | 81.1 | 666.669 qps |
| - deploy=True | 9.42M | 1.74G | 256 | 79.9 | 881.429 qps |
| [FastViT_SA12](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa12_imagenet.h5) | 11.58M | 1.88G | 256 | 80.9 | 656.95 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa12_distill.h5) | 11.58M | 1.88G | 256 | 81.9 | 656.95 qps |
| - deploy=True | 11.54M | 1.88G | 256 | 80.9 | 833.011 qps |
| [FastViT_SA24](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa24_imagenet.h5) | 21.55M | 3.66G | 256 | 82.7 | 371.84 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa24_distill.h5) | 21.55M | 3.66G | 256 | 83.4 | 371.84 qps |
| - deploy=True | 21.49M | 3.66G | 256 | 82.7 | 444.055 qps |
| [FastViT_SA36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa36_imagenet.h5) | 31.53M | 5.44G | 256 | 83.6 | 267.986 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_sa36_distill.h5) | 31.53M | 5.44G | 256 | 84.2 | 267.986 qps |
| - deploy=True | 31.44M | 5.43G | 256 | 83.6 | 325.967 qps |
| [FastViT_MA36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_ma36_imagenet.h5) | 44.07M | 7.64G | 256 | 83.9 | 211.928 qps |
| - [distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/fastvit/fastvit_ma36_distill.h5) | 44.07M | 7.64G | 256 | 84.6 | 211.928 qps |
| - deploy=True | 43.96M | 7.63G | 256 | 83.9 | 274.559 qps |
## FBNetV3
- [Keras FBNetV3](keras_cv_attention_models/mobilenetv3_family#fbnetv3) includes implementation of [PDF 2006.02049 FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining](https://arxiv.org/pdf/2006.02049.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------- | ------ | -------- | ----- | -------- | ------------ |
| [FBNetV3B](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/fbnetv3_b_imagenet.h5) | 5.57M | 539.82M | 256 | 79.15 | 713.882 qps |
| [FBNetV3D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/fbnetv3_d_imagenet.h5) | 10.31M | 665.02M | 256 | 79.68 | 635.963 qps |
| [FBNetV3G](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/fbnetv3_g_imagenet.h5) | 16.62M | 1379.30M | 256 | 82.05 | 478.835 qps |
## FlexiViT
- [Keras FlexiViT](keras_cv_attention_models/beit) includes models from [PDF 2212.08013 FlexiViT: One Model for All Patch Sizes](https://arxiv.org/pdf/2212.08013.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------- | ------ | ----- | -------- | ------------ |
| [FlexiViTSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/flexivit_small_240_imagenet.h5) | 22.06M | 5.36G | 240 | 82.53 | 744.578 qps |
| [FlexiViTBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/flexivit_base_240_imagenet.h5) | 86.59M | 20.33G | 240 | 84.66 | 301.948 qps |
| [FlexiViTLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/flexivit_large_240_imagenet.h5) | 304.47M | 71.09G | 240 | 85.64 | 105.187 qps |
## GCViT
- [Keras GCViT](keras_cv_attention_models/gcvit) includes implementation of [PDF 2206.09959 Global Context Vision Transformers](https://arxiv.org/pdf/2206.09959.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | Download |
| --------------- | ------ | ------ | ----- | -------- | -------- |
| [GCViT_XXTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_xx_tiny_224_imagenet.h5) | 12.0M | 2.15G | 224 | 79.9 | 337.7 qps |
| [GCViT_XTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_x_tiny_224_imagenet.h5) | 20.0M | 2.96G | 224 | 82.0 | 255.625 qps |
| [GCViT_Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_tiny_224_imagenet.h5) | 28.2M | 4.83G | 224 | 83.5 | 174.553 qps |
| [GCViT_Tiny2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_tiny2_224_imagenet.h5) | 34.5M | 6.28G | 224 | 83.7 | |
| [GCViT_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_small_224_imagenet.h5) | 51.1M | 8.63G | 224 | 84.3 | 131.577 qps |
| [GCViT_Small2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_small2_224_imagenet.h5) | 68.6M | 11.7G | 224 | 84.8 | |
| [GCViT_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_base_224_imagenet.h5) | 90.3M | 14.9G | 224 | 85.0 | 105.845 qps |
| [GCViT_Large](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_large_224_imagenet.h5) | 202.1M | 32.8G | 224 | 85.7 | |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_large_224_imagenet21k-ft1k.h5) | 202.1M | 32.8G | 224 | 86.6 | |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_large_384_imagenet21k-ft1k.h5) | 202.9M | 105.1G | 384 | 87.4 | |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gcvit/gcvit_large_512_imagenet21k-ft1k.h5) | 203.8M | 205.1G | 512 | 87.6 | |
## GhostNet
- [Keras GhostNet](keras_cv_attention_models/ghostnet) includes implementation of [PDF 1911.11907 GhostNet: More Features from Cheap Operations](https://arxiv.org/pdf/1911.11907.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------ | ------ | ------ | ----- | -------- | ------------ |
| [GhostNet_050](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnet_050_imagenet.h5) | 2.59M | 42.6M | 224 | 66.88 | 1272.25 qps |
| [GhostNet_100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnet_100_imagenet.h5) | 5.18M | 141.7M | 224 | 74.16 | 1167.4 qps |
| [GhostNet_130](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnet_130_imagenet.h5) | 7.36M | 227.7M | 224 | 75.79 | 1024.49 qps |
| - [ssld](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnet_130_ssld.h5) | 7.36M | 227.7M | 224 | 79.38 | 1024.49 qps |
## GhostNetV2
- [Keras GhostNet](keras_cv_attention_models/ghostnet) includes implementation of [PDF GhostNetV2: Enhance Cheap Operation with Long-Range Attention](https://openreview.net/pdf/6db544c65bbd0fa7d7349508454a433c112470e2.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------- | ------ | ------ | ----- | -------- | ------------ |
| [GhostNetV2_100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnetv2_100_imagenet.h5) | 6.12M | 168.5M | 224 | 75.3 | 797.088 qps |
| [GhostNetV2_130](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnetv2_130_imagenet.h5) | 8.96M | 271.1M | 224 | 76.9 | 722.668 qps |
| [GhostNetV2_160](https://github.com/leondgarse/keras_cv_attention_models/releases/download/ghostnetv2/ghostnetv2_160_imagenet.h5) | 12.39M | 400.9M | 224 | 77.8 | 572.268 qps |
## GMLP
- [Keras GMLP](keras_cv_attention_models/mlp_family#gmlp) includes implementation of [PDF 2105.08050 Pay Attention to MLPs](https://arxiv.org/pdf/2105.08050.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------- | ------ | ------ | ----- | -------- | ------------ |
| GMLPTiny16 | 6M | 1.35G | 224 | 72.3 | 234.187 qps |
| [GMLPS16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/gmlp_s16_imagenet.h5) | 20M | 4.44G | 224 | 79.6 | 138.363 qps |
| GMLPB16 | 73M | 15.82G | 224 | 81.6 | 77.816 qps |
## GPViT
- [Keras GPViT](keras_cv_attention_models/gpvit) includes implementation of [PDF 2212.06795 GPVIT: A HIGH RESOLUTION NON-HIERARCHICAL VISION TRANSFORMER WITH GROUP PROPAGATION](https://arxiv.org/pdf/2212.06795.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------- | ------ | ------ | ----- | -------- | ------------ |
| [GPViT_L1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpvit/gpvit_l1_224_imagenet.h5) | 9.59M | 6.15G | 224 | 80.5 | 210.166 qps |
| [GPViT_L2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpvit/gpvit_l2_224_imagenet.h5) | 24.2M | 15.74G | 224 | 83.4 | 139.656 qps |
| [GPViT_L3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpvit/gpvit_l3_224_imagenet.h5) | 36.7M | 23.54G | 224 | 84.1 | 131.284 qps |
| [GPViT_L4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpvit/gpvit_l4_224_imagenet.h5) | 75.5M | 48.29G | 224 | 84.3 | 94.1899 qps |
## HaloNet
- [Keras HaloNet](keras_cv_attention_models/halonet) is for [PDF 2103.12731 Scaling Local Self-Attention for Parameter Efficient Visual Backbones](https://arxiv.org/pdf/2103.12731.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------- | ------ | ------- | ----- | -------- | ------------ |
| [HaloNextECA26T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/halonext_eca26t_256_imagenet.h5) | 10.7M | 2.43G | 256 | 79.50 | 1028.93 qps |
| [HaloNet26T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/halonet26t_256_imagenet.h5) | 12.5M | 3.18G | 256 | 79.13 | 1096.79 qps |
| [HaloNetSE33T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/halonet_se33t_256_imagenet.h5) | 13.7M | 3.55G | 256 | 80.99 | 582.008 qps |
| [HaloRegNetZB](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/haloregnetz_b_224_imagenet.h5) | 11.68M | 1.97G | 224 | 81.042 | 575.961 qps |
| [HaloNet50T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/halonet50t_256_imagenet.h5) | 22.7M | 5.29G | 256 | 81.70 | 512.677 qps |
| [HaloBotNet50T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/halonet/halobotnet50t_256_imagenet.h5) | 22.6M | 5.02G | 256 | 82.0 | 431.616 qps |
## Hiera
- [Keras Hiera](keras_cv_attention_models/hiera) is for [PDF 2306.00989 Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/pdf/2306.00989.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------------------------- | ------- | ------- | ----- | -------- | ------------ |
| [HieraTiny, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_tiny_224_mae_in1k_ft1k.h5) | 27.91M | 4.93G | 224 | 82.8 | 644.356 qps |
| [HieraSmall, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_small_224_mae_in1k_ft1k.h5) | 35.01M | 6.44G | 224 | 83.8 | 491.669 qps |
| [HieraBase, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_base_224_mae_in1k_ft1k.h5) | 51.52M | 9.43G | 224 | 84.5 | 351.542 qps |
| [HieraBasePlus, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_base_plus_224_mae_in1k_ft1k.h5) | 69.90M | 12.71G | 224 | 85.2 | 291.446 qps |
| [HieraLarge, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_large_224_mae_in1k_ft1k.h5) | 213.74M | 40.43G | 224 | 86.1 | 111.042 qps |
| [HieraHuge, mae_in1k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hiera/hiera_huge_224_mae_in1k_ft1k.h5) | 672.78M | 125.03G | 224 | 86.9 | |
## HorNet
- [Keras HorNet](keras_cv_attention_models/hornet) is for [PDF 2207.14284 HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions](https://arxiv.org/pdf/2207.14284.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ------ | ----- | -------- | ------------ |
| [HorNetTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_tiny_224_imagenet.h5) | 22.4M | 4.01G | 224 | 82.8 | 222.665 qps |
| [HorNetTinyGF](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_tiny_gf_224_imagenet.h5) | 23.0M | 3.94G | 224 | 83.0 | |
| [HorNetSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_small_224_imagenet.h5) | 49.5M | 8.87G | 224 | 83.8 | 166.998 qps |
| [HorNetSmallGF](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_small_gf_224_imagenet.h5) | 50.4M | 8.77G | 224 | 84.0 | |
| [HorNetBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_base_224_imagenet.h5) | 87.3M | 15.65G | 224 | 84.2 | 133.842 qps |
| [HorNetBaseGF](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_base_gf_224_imagenet.h5) | 88.4M | 15.51G | 224 | 84.3 | |
| [HorNetLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_large_224_imagenet22k.h5) | 194.5M | 34.91G | 224 | 86.8 | 89.8254 qps |
| [HorNetLargeGF](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_large_gf_224_imagenet22k.h5) | 196.3M | 34.72G | 224 | 87.0 | |
| [HorNetLargeGF](https://github.com/leondgarse/keras_cv_attention_models/releases/download/hornet/hornet_large_gf_384_imagenet22k.h5) | 201.8M | 102.0G | 384 | 87.7 | |
## IFormer
- [Keras IFormer](keras_cv_attention_models/iformer) is for [PDF 2205.12956 Inception Transformer](https://arxiv.org/pdf/2205.12956.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------ | ------ | ------ | ----- | -------- | ------------ |
| [IFormerSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_small_224_imagenet.h5) | 19.9M | 4.88G | 224 | 83.4 | 254.392 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_small_384_imagenet.h5) | 20.9M | 16.29G | 384 | 84.6 | 128.98 qps |
| [IFormerBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_base_224_imagenet.h5) | 47.9M | 9.44G | 224 | 84.6 | 147.868 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_base_384_imagenet.h5) | 48.9M | 30.86G | 384 | 85.7 | 77.8391 qps |
| [IFormerLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_largel_224_imagenet.h5) | 86.6M | 14.12G | 224 | 84.6 | 113.434 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/iformer/iformer_largel_384_imagenet.h5) | 87.7M | 45.74G | 384 | 85.8 | 60.0292 qps |
## InceptionNeXt
- [Keras InceptionNeXt](keras_cv_attention_models/inceptionnext) is for [PDF 2303.16900 InceptionNeXt: When Inception Meets ConvNeXt](https://arxiv.org/pdf/2303.16900.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------ | ------ | ----- | -------- | ------------ |
| [InceptionNeXtTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/inceptionnext/inceptionnext_tiny_imagenet.h5) | 28.05M | 4.21G | 224 | 82.3 | 606.527 qps |
| [InceptionNeXtSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/inceptionnext/inceptionnext_small_imagenet.h5) | 49.37M | 8.39G | 224 | 83.5 | 329.01 qps |
| [InceptionNeXtBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/inceptionnext/inceptionnext_base_224_imagenet.h5) | 86.67M | 14.88G | 224 | 84.0 | 260.639 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/inceptionnext/inceptionnext_base_384_imagenet.h5) | 86.67M | 43.73G | 384 | 85.2 | 142.888 qps |
## LCNet
- [Keras LCNet](keras_cv_attention_models/mobilenetv3_family#lcnet) includes implementation of [PDF 2109.15099 PP-LCNet: A Lightweight CPU Convolutional Neural Network](https://arxiv.org/pdf/2109.15099.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------- | ------ | ------- | ----- | -------- | ------------ |
| [LCNet050](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_050_imagenet.h5) | 1.88M | 46.02M | 224 | 63.10 | 3107.89 qps |
| - [ssld](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_050_ssld.h5) | 1.88M | 46.02M | 224 | 66.10 | 3107.89 qps |
| [LCNet075](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_075_imagenet.h5) | 2.36M | 96.82M | 224 | 68.82 | 3083.55 qps |
| [LCNet100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_100_imagenet.h5) | 2.95M | 158.28M | 224 | 72.10 | 2752.6 qps |
| - [ssld](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_100_ssld.h5) | 2.95M | 158.28M | 224 | 74.39 | 2752.6 qps |
| [LCNet150](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_150_imagenet.h5) | 4.52M | 338.05M | 224 | 73.71 | 2250.69 qps |
| [LCNet200](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_200_imagenet.h5) | 6.54M | 585.35M | 224 | 75.18 | 2028.31 qps |
| [LCNet250](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_250_imagenet.h5) | 9.04M | 900.16M | 224 | 76.60 | 1686.7 qps |
| - [ssld](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/lcnet_250_ssld.h5) | 9.04M | 900.16M | 224 | 80.82 | 1686.7 qps |
## LeViT
- [Keras LeViT](keras_cv_attention_models/levit) is for [PDF 2104.01136 LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference](https://arxiv.org/pdf/2104.01136.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------ | ----- | ----- | -------- | ------------ |
| [LeViT128S, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/levit128s_imagenet.h5) | 7.8M | 0.31G | 224 | 76.6 | 800.53 qps |
| [LeViT128, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/levit128_imagenet.h5) | 9.2M | 0.41G | 224 | 78.6 | 628.714 qps |
| [LeViT192, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/levit192_imagenet.h5) | 11M | 0.66G | 224 | 80.0 | 597.299 qps |
| [LeViT256, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/levit256_imagenet.h5) | 19M | 1.13G | 224 | 81.6 | 538.885 qps |
| [LeViT384, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/levit384_imagenet.h5) | 39M | 2.36G | 224 | 82.6 | 460.139 qps |
## MaxViT
- [Keras MaxViT](keras_cv_attention_models/maxvit) is for [PDF 2204.01697 MaxViT: Multi-Axis Vision Transformer](https://arxiv.org/pdf/2204.01697.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [MaxViT_Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_tiny_224_imagenet.h5) | 31M | 5.6G | 224 | 83.62 | 195.283 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_tiny_384_imagenet.h5) | 31M | 17.7G | 384 | 85.24 | 92.5725 qps |
| - [512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_tiny_512_imagenet.h5) | 31M | 33.7G | 512 | 85.72 | 52.6485 qps |
| [MaxViT_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_small_224_imagenet.h5) | 69M | 11.7G | 224 | 84.45 | 149.286 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_small_384_imagenet.h5) | 69M | 36.1G | 384 | 85.74 | 61.5757 qps |
| - [512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_small_512_imagenet.h5) | 69M | 67.6G | 512 | 86.19 | 34.7002 qps |
| [MaxViT_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_224_imagenet.h5) | 119M | 24.2G | 224 | 84.95 | 74.7351 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_384_imagenet.h5) | 119M | 74.2G | 384 | 86.34 | 31.9028 qps |
| - [512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_512_imagenet.h5) | 119M | 138.5G | 512 | 86.66 | 17.8139 qps |
| - [imagenet21k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_224_imagenet21k.h5) | 135M | 24.2G | 224 | | 74.7351 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_384_imagenet21k-ft1k.h5) | 119M | 74.2G | 384 | 88.24 | 31.9028 qps |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_base_512_imagenet21k-ft1k.h5) | 119M | 138.5G | 512 | 88.38 | 17.8139 qps |
| [MaxViT_Large](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_224_imagenet.h5) | 212M | 43.9G | 224 | 85.17 | 58.0967 qps |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_384_imagenet.h5) | 212M | 133.1G | 384 | 86.40 | 24.1388 qps |
| - [512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_512_imagenet.h5) | 212M | 245.4G | 512 | 86.70 | 13.063 qps |
| - [imagenet21k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_224_imagenet21k.h5) | 233M | 43.9G | 224 | | 58.0967 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_384_imagenet21k-ft1k.h5) | 212M | 133.1G | 384 | 88.32 | 24.1388 qps |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_large_512_imagenet21k-ft1k.h5) | 212M | 245.4G | 512 | 88.46 | 13.063 qps |
| [MaxViT_XLarge, imagenet21k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_xlarge_224_imagenet21k.h5) | 507M | 97.7G | 224 | | |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_xlarge_384_imagenet21k-ft1k.h5) | 475M | 293.7G | 384 | 88.51 | |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/maxvit/maxvit_xlarge_512_imagenet21k-ft1k.h5) | 475M | 535.2G | 512 | 88.70 | |
## MetaTransFormer
- [Keras MetaTransFormer](keras_cv_attention_models/beit) includes models from [PDF 2307.10802 Meta-Transformer: A Unified Framework for Multimodal Learning](https://arxiv.org/abs/2307.10802).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------------------- | ------- | ------ | ----- | -------- | ------------ |
| [MetaTransformerBasePatch16, laion_2b](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/meta_transformer_base_patch16_384_laion_2b.h5) | 86.86M | 55.73G | 384 | 85.4 | 150.731 qps |
| [MetaTransformerLargePatch14, laion_2b](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/meta_transformer_large_patch14_336_laion_2b.h5) | 304.53M | 191.6G | 336 | 88.1 | 50.1536 qps |
## MLP mixer
- [Keras MLP mixer](keras_cv_attention_models/mlp_family#mlp-mixer) includes implementation of [PDF 2105.01601 MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/pdf/2105.01601.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------------- | ------ | ------- | ----- | -------- | ------------ |
| MLPMixerS32, JFT | 19.1M | 1.01G | 224 | 68.70 | 488.839 qps |
| MLPMixerS16, JFT | 18.5M | 3.79G | 224 | 73.83 | 451.962 qps |
| MLPMixerB32, JFT | 60.3M | 3.25G | 224 | 75.53 | 247.629 qps |
| - [sam](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_b32_imagenet_sam.h5) | 60.3M | 3.25G | 224 | 72.47 | 247.629 qps |
| [MLPMixerB16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_b16_imagenet.h5) | 59.9M | 12.64G | 224 | 76.44 | 207.423 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_b16_imagenet21k.h5) | 59.9M | 12.64G | 224 | 80.64 | 207.423 qps |
| - [sam](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_b16_imagenet_sam.h5) | 59.9M | 12.64G | 224 | 77.36 | 207.423 qps |
| - JFT | 59.9M | 12.64G | 224 | 80.00 | 207.423 qps |
| MLPMixerL32, JFT | 206.9M | 11.30G | 224 | 80.67 | 95.1865 qps |
| [MLPMixerL16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_l16_imagenet.h5) | 208.2M | 44.66G | 224 | 71.76 | 77.9928 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/mlp_mixer_l16_imagenet21k.h5) | 208.2M | 44.66G | 224 | 82.89 | 77.9928 qps |
| - JFT | 208.2M | 44.66G | 224 | 84.82 | 77.9928 qps |
| - 448 | 208.2M | 178.54G | 448 | 83.91 | |
| - 448, JFT | 208.2M | 178.54G | 448 | 86.78 | |
| MLPMixerH14, JFT | 432.3M | 121.22G | 224 | 86.32 | |
| - 448, JFT | 432.3M | 484.73G | 448 | 87.94 | |
## MobileNetV3
- [Keras MobileNetV3](keras_cv_attention_models/mobilenetv3_family#mobilenetv3) includes implementation of [PDF 1905.02244 Searching for MobileNetV3](https://arxiv.org/pdf/1905.02244.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------- | ------ | ------- | ----- | -------- | ------------ |
| [MobileNetV3Small050](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_small_050_imagenet.h5) | 1.29M | 24.92M | 224 | 57.89 | 2458.28 qps |
| [MobileNetV3Small075](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_small_075_imagenet.h5) | 2.04M | 44.35M | 224 | 65.24 | 2286.44 qps |
| [MobileNetV3Small100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_small_100_imagenet.h5) | 2.54M | 57.62M | 224 | 67.66 | 2058.06 qps |
| [MobileNetV3Large075](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_large_075_imagenet.h5) | 3.99M | 156.30M | 224 | 73.44 | 1643.78 qps |
| [MobileNetV3Large100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_large_100_imagenet.h5) | 5.48M | 218.73M | 224 | 75.77 | 1629.44 qps |
| - [miil](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/mobilenetv3_large_100_mill.h5) | 5.48M | 218.73M | 224 | 77.92 | 1629.44 qps |
## MobileViT
- [Keras MobileViT](keras_cv_attention_models/mobilevit) is for [PDF 2110.02178 MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER](https://arxiv.org/pdf/2110.02178.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ----- | ----- | -------- | ------------ |
| [MobileViT_XXS](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_xxs_imagenet.h5) | 1.3M | 0.42G | 256 | 69.0 | 1319.43 qps |
| [MobileViT_XS](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_xs_imagenet.h5) | 2.3M | 1.05G | 256 | 74.7 | 1019.57 qps |
| [MobileViT_S](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_s_imagenet.h5) | 5.6M | 2.03G | 256 | 78.3 | 790.943 qps |
## MobileViT_V2
- [Keras MobileViT_V2](keras_cv_attention_models/mobilevit) is for [PDF 2206.02680 Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/pdf/2206.02680.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------ | ------ | ----- | ----- | -------- | ------------ |
| [MobileViT_V2_050](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_050_256_imagenet.h5) | 1.37M | 0.47G | 256 | 70.18 | 718.337 qps |
| [MobileViT_V2_075](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_075_256_imagenet.h5) | 2.87M | 1.04G | 256 | 75.56 | 642.323 qps |
| [MobileViT_V2_100](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_100_256_imagenet.h5) | 4.90M | 1.83G | 256 | 78.09 | 591.217 qps |
| [MobileViT_V2_125](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_125_256_imagenet.h5) | 7.48M | 2.84G | 256 | 79.65 | 510.25 qps |
| [MobileViT_V2_150](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_150_256_imagenet.h5) | 10.6M | 4.07G | 256 | 80.38 | 466.482 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_150_256_imagenet22k.h5) | 10.6M | 4.07G | 256 | 81.46 | 466.482 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_150_384_imagenet22k.h5) | 10.6M | 9.15G | 384 | 82.60 | 278.834 qps |
| [MobileViT_V2_175](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_175_256_imagenet.h5) | 14.3M | 5.52G | 256 | 80.84 | 412.759 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_175_256_imagenet22k.h5) | 14.3M | 5.52G | 256 | 81.94 | 412.759 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_175_384_imagenet22k.h5) | 14.3M | 12.4G | 384 | 82.93 | 247.108 qps |
| [MobileViT_V2_200](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_200_256_imagenet.h5) | 18.4M | 7.12G | 256 | 81.17 | 394.325 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_200_256_imagenet22k.h5) | 18.4M | 7.12G | 256 | 82.36 | 394.325 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilevit/mobilevit_v2_200_384_imagenet22k.h5) | 18.4M | 16.2G | 384 | 83.41 | 229.399 qps |
## MogaNet
- [Keras MogaNet](keras_cv_attention_models/moganet) is for [PDF 2211.03295 Efficient Multi-order Gated Aggregation Network](https://arxiv.org/pdf/2211.03295.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------ | ------ | ------ | ----- | -------- | ------------ |
| [MogaNetXtiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_xtiny_imagenet.h5) | 2.96M | 806M | 224 | 76.5 | 398.488 qps |
| [MogaNetTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_tiny_224_imagenet.h5) | 5.20M | 1.11G | 224 | 79.0 | 362.409 qps |
| - [256](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_tiny_256_imagenet.h5) | 5.20M | 1.45G | 256 | 79.6 | 335.372 qps |
| [MogaNetSmall](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_small_imagenet.h5) | 25.3M | 4.98G | 224 | 83.4 | 249.807 qps |
| [MogaNetBase](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_base_imagenet.h5) | 43.7M | 9.96G | 224 | 84.2 | 133.071 qps |
| [MogaNetLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/moganet/moganet_large_imagenet.h5) | 82.5M | 15.96G | 224 | 84.6 | 84.2045 qps |
## NAT
- [Keras NAT](keras_cv_attention_models/nat) is for [PDF 2204.07143 Neighborhood Attention Transformer](https://arxiv.org/pdf/2204.07143.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------- | ------ | ------ | ----- | -------- | ------------ |
| [NAT_Mini](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/nat_mini_imagenet.h5) | 20.0M | 2.73G | 224 | 81.8 | 85.2324 qps |
| [NAT_Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/nat_tiny_imagenet.h5) | 27.9M | 4.34G | 224 | 83.2 | 62.6147 qps |
| [NAT_Small](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/nat_small_imagenet.h5) | 50.7M | 7.84G | 224 | 83.7 | 41.1545 qps |
| [NAT_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nat/nat_base_imagenet.h5) | 89.8M | 13.76G | 224 | 84.3 | 30.8989 qps |
## NFNets
- [Keras NFNets](keras_cv_attention_models/nfnets) is for [PDF 2102.06171 High-Performance Large-Scale Image Recognition Without Normalization](https://arxiv.org/pdf/2102.06171.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------ | ------ | ------- | ----- | -------- | ------------ |
| [NFNetL0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetl0_imagenet.h5) | 35.07M | 7.13G | 288 | 82.75 | |
| [NFNetF0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf0_imagenet.h5) | 71.5M | 12.58G | 256 | 83.6 | |
| [NFNetF1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf1_imagenet.h5) | 132.6M | 35.95G | 320 | 84.7 | |
| [NFNetF2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf2_imagenet.h5) | 193.8M | 63.24G | 352 | 85.1 | |
| [NFNetF3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf3_imagenet.h5) | 254.9M | 115.75G | 416 | 85.7 | |
| [NFNetF4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf4_imagenet.h5) | 316.1M | 216.78G | 512 | 85.9 | |
| [NFNetF5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf5_imagenet.h5) | 377.2M | 291.73G | 544 | 86.0 | |
| [NFNetF6, sam](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/nfnetf6_imagenet.h5) | 438.4M | 379.75G | 576 | 86.5 | |
| NFNetF7 | 499.5M | 481.80G | 608 | | |
| [ECA_NFNetL0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/eca_nfnetl0_imagenet.h5) | 24.14M | 7.12G | 288 | 82.58 | |
| [ECA_NFNetL1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/eca_nfnetl1_imagenet.h5) | 41.41M | 14.93G | 320 | 84.01 | |
| [ECA_NFNetL2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/nfnets/eca_nfnetl2_imagenet.h5) | 56.72M | 30.12G | 384 | 84.70 | |
| ECA_NFNetL3 | 72.04M | 52.73G | 448 | | |
## PVT_V2
- [Keras PVT_V2](keras_cv_attention_models/pvt) is for [PDF 2106.13797 PVTv2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/pdf/2106.13797.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------------- | ------ | ------ | ----- | -------- | ------------ |
| [PVT_V2B0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b0_imagenet.h5) | 3.7M | 580.3M | 224 | 70.5 | 561.593 qps |
| [PVT_V2B1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b1_imagenet.h5) | 14.0M | 2.14G | 224 | 78.7 | 392.408 qps |
| [PVT_V2B2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b2_imagenet.h5) | 25.4M | 4.07G | 224 | 82.0 | 210.476 qps |
| [PVT_V2B2_linear](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b2_linear_imagenet.h5) | 22.6M | 3.94G | 224 | 82.1 | 226.791 qps |
| [PVT_V2B3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b3_imagenet.h5) | 45.2M | 6.96G | 224 | 83.1 | 135.51 qps |
| [PVT_V2B4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b4_imagenet.h5) | 62.6M | 10.19G | 224 | 83.6 | 97.666 qps |
| [PVT_V2B5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/pvt/pvt_v2_b5_imagenet.h5) | 82.0M | 11.81G | 224 | 83.8 | 81.4798 qps |
## RegNetY
- [Keras RegNetY](keras_cv_attention_models/resnet_family#regnety) is for [PDF 2003.13678 Designing Network Design Spaces](https://arxiv.org/pdf/2003.13678.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------- | ------- | ------ | ----- | -------- | ------------ |
| [RegNetY040](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnety_040_imagenet.h5) | 20.65M | 3.98G | 224 | 82.3 | 749.277 qps |
| [RegNetY064](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnety_064_imagenet.h5) | 30.58M | 6.36G | 224 | 83.0 | 436.946 qps |
| [RegNetY080](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnety_080_imagenet.h5) | 39.18M | 7.97G | 224 | 83.17 | 513.43 qps |
| [RegNetY160](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnety_160_imagenet.h5) | 83.59M | 15.92G | 224 | 82.0 | 338.046 qps |
| [RegNetY320](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnety_320_imagenet.h5) | 145.05M | 32.29G | 224 | 82.5 | 188.508 qps |
## RegNetZ
- [Keras RegNetZ](keras_cv_attention_models/resnet_family#regnetz) includes implementation of [Github timm/models/byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py).
- Related paper [PDF 2004.02967 Evolving Normalization-Activation Layers](https://arxiv.org/pdf/2004.02967.pdf)
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------- | ------ | ----- | ----- | -------- | ------------ |
| [RegNetZB16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_b16_imagenet.h5) | 9.72M | 1.44G | 224 | 79.868 | 751.035 qps |
| [RegNetZC16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_c16_imagenet.h5) | 13.46M | 2.50G | 256 | 82.164 | 636.549 qps |
| [RegNetZC16_EVO](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_c16_evo_imagenet.h5) | 13.49M | 2.55G | 256 | 81.9 | |
| [RegNetZD32](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_d32_imagenet.h5) | 27.58M | 5.96G | 256 | 83.422 | 459.204 qps |
| [RegNetZD8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_d8_imagenet.h5) | 23.37M | 3.95G | 256 | 83.5 | 460.021 qps |
| [RegNetZD8_EVO](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_d8_evo_imagenet.h5) | 23.46M | 4.61G | 256 | 83.42 | |
| [RegNetZE8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/regnetz_e8_imagenet.h5) | 57.70M | 9.88G | 256 | 84.5 | 274.97 qps |
## RepViT
- [Keras RepViT](keras_cv_attention_models/repvit) is for [PDF 2307.09283 RepViT: Revisiting Mobile CNN From ViT Perspective](https://arxiv.org/pdf/2307.09283.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------ | ------ | ----- | ----- | -------- | -------- |
| [RepViT_M09, distillation](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/repvit_m_09_imagenet.h5) | 5.10M | 0.82G | 224 | 79.1 | |
| - deploy=True | 5.07M | 0.82G | 224 | 79.1 | 966.72 qps |
| [RepViT_M10, distillation](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/repvit_m_10_imagenet.h5) | 6.85M | 1.12G | 224 | 80.3 | 1157.8 qps |
| - deploy=True | 6.81M | 1.12G | 224 | 80.3 | |
| [RepViT_M11, distillation](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/repvit_m_11_imagenet.h5) | 8.29M | 1.35G | 224 | 81.2 | 846.682 qps |
| - deploy=True | 8.24M | 1.35G | 224 | 81.2 | 1027.5 qps |
| [RepViT_M15, distillation](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/repvit_m_15_imagenet.h5) | 14.13M | 2.30G | 224 | 82.5 | |
| - deploy=True | 14.05M | 2.30G | 224 | 82.5 | |
| [RepViT_M23, distillation](https://github.com/leondgarse/keras_cv_attention_models/releases/download/levit/repvit_m_23_imagenet.h5) | 23.01M | 4.55G | 224 | 83.7 | |
| - deploy=True | 22.93M | 4.55G | 224 | 83.7 | |
## ResMLP
- [Keras ResMLP](keras_cv_attention_models/mlp_family#resmlp) includes implementation of [PDF 2105.03404 ResMLP: Feedforward networks for image classification with data-efficient training](https://arxiv.org/pdf/2105.03404.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ------- | ----- | -------- | ------------ |
| [ResMLP12](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/resmlp12_imagenet.h5) | 15M | 3.02G | 224 | 77.8 | 928.402 qps |
| [ResMLP24](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/resmlp24_imagenet.h5) | 30M | 5.98G | 224 | 80.8 | 420.709 qps |
| [ResMLP36](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/resmlp36_imagenet.h5) | 116M | 8.94G | 224 | 81.1 | 309.513 qps |
| [ResMLP_B24](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/resmlp_b24_imagenet.h5) | 129M | 100.39G | 224 | 83.6 | 78.3015 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/resmlp_b24_imagenet22k.h5) | 129M | 100.39G | 224 | 84.4 | 78.3015 qps |
## ResNeSt
- [Keras ResNeSt](keras_cv_attention_models/resnest) is for [PDF 2004.08955 ResNeSt: Split-Attention Networks](https://arxiv.org/pdf/2004.08955.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------- | ------ | ------ | ----- | -------- | ------------ |
| [ResNest50](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnest/resnest50_imagenet.h5) | 28M | 5.38G | 224 | 81.03 | 534.627 qps |
| [ResNest101](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnest/resnest101_imagenet.h5) | 49M | 13.33G | 256 | 82.83 | 257.074 qps |
| [ResNest200](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnest/resnest200_imagenet.h5) | 71M | 35.55G | 320 | 83.84 | 118.183 qps |
| [ResNest269](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnest/resnest269_imagenet.h5) | 111M | 77.42G | 416 | 84.54 | 61.167 qps |
## ResNetD
- [Keras ResNetD](keras_cv_attention_models/resnet_family#resnetd) includes implementation of [PDF 1812.01187 Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/pdf/1812.01187.pdf)
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ---------- | ------ | ------ | ----- | -------- | ------------ |
| [ResNet50D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnet50d_imagenet.h5) | 25.58M | 4.33G | 224 | 80.530 | 930.214 qps |
| [ResNet101D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnet101d_imagenet.h5) | 44.57M | 8.04G | 224 | 83.022 | 502.268 qps |
| [ResNet152D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnet152d_imagenet.h5) | 60.21M | 11.75G | 224 | 83.680 | 353.279 qps |
| [ResNet200D](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnet200d_imagenet.h5) | 64.69M | 15.25G | 224 | 83.962 | 287.73 qps |
## ResNetQ
- [Keras ResNetQ](keras_cv_attention_models/resnet_family#resnetq) includes implementation of [Github timm/models/resnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py)
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------- | ------ | ----- | ----- | -------- | ------------ |
| [ResNet51Q](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnet51q_imagenet.h5) | 35.7M | 4.87G | 224 | 82.36 | 838.754 qps |
| ResNet61Q | 36.8M | 5.96G | 224 | | 730.245 qps |
## ResNeXt
- [Keras ResNeXt](keras_cv_attention_models/resnet_family#resnext) includes implementation of [PDF 1611.05431 Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
- `SWSL` means `Semi-Weakly Supervised ResNe*t` from [Github facebookresearch/semi-supervised-ImageNet1K-models](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models). **Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only**.
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------------- | ------ | ------ | ----- | -------- | ------------ |
| [ResNeXt50, (32x4d)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext50_imagenet.h5) | 25M | 4.23G | 224 | 79.768 | 1041.46 qps |
| - [SWSL](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext50_swsl.h5) | 25M | 4.23G | 224 | 82.182 | 1041.46 qps |
| [ResNeXt50D, (32x4d + deep)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext50d_imagenet.h5) | 25M | 4.47G | 224 | 79.676 | 1010.94 qps |
| [ResNeXt101, (32x4d)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext101_imagenet.h5) | 42M | 7.97G | 224 | 80.334 | 571.652 qps |
| - [SWSL](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext101_swsl.h5) | 42M | 7.97G | 224 | 83.230 | 571.652 qps |
| [ResNeXt101W, (32x8d)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext101_imagenet.h5) | 89M | 16.41G | 224 | 79.308 | 367.431 qps |
| - [SWSL](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext101w_swsl.h5) | 89M | 16.41G | 224 | 84.284 | 367.431 qps |
| [ResNeXt101W_64, (64x4d)](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/resnext101w_64_imagenet.h5) | 83.46M | 15.46G | 224 | 82.46 | 377.83 qps |
## SwinTransformerV2
- [Keras SwinTransformerV2](keras_cv_attention_models/swin_transformer_v2) includes implementation of [PDF 2111.09883 Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/pdf/2111.09883.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------------------------------ | ------ | ------ | ----- | -------- | ------------ |
| [SwinTransformerV2Tiny_ns](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_tiny_ns_224_imagenet.h5) | 28.3M | 4.69G | 224 | 81.8 | 289.205 qps |
| [SwinTransformerV2Small_ns](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_small_ns_224_imagenet.h5) | 49.7M | 9.12G | 224 | 83.5 | 169.645 qps |
| [SwinTransformerV2Tiny_window8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_tiny_window8_256_imagenet.h5) | 28.3M | 5.99G | 256 | 81.8 | 275.547 qps |
| [SwinTransformerV2Tiny_window16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_tiny_window16_256_imagenet.h5) | 28.3M | 6.75G | 256 | 82.8 | 217.207 qps |
| [SwinTransformerV2Small_window8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_small_window8_256_imagenet.h5) | 49.7M | 11.63G | 256 | 83.7 | 157.559 qps |
| [SwinTransformerV2Small_window16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_small_window16_256_imagenet.h5) | 49.7M | 12.93G | 256 | 84.1 | 129.953 qps |
| [SwinTransformerV2Base_window8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_base_window8_256_imagenet.h5) | 87.9M | 20.44G | 256 | 84.2 | 126.294 qps |
| [SwinTransformerV2Base_window16](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_base_window16_256_imagenet.h5) | 87.9M | 22.17G | 256 | 84.6 | 99.634 qps |
| [SwinTransformerV2Base_window16, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_base_window16_256_imagenet22k.h5) | 87.9M | 22.17G | 256 | 86.2 | 99.634 qps |
| [SwinTransformerV2Base_window24, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_base_window24_384_imagenet22k.h5) | 87.9M | 55.89G | 384 | 87.1 | 35.0508 qps |
| [SwinTransformerV2Large_window16, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_large_window16_256_imagenet22k.h5) | 196.7M | 48.03G | 256 | 86.9 | |
| [SwinTransformerV2Large_window24, 21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/swin_transformer_v2/swin_transformer_v2_large_window24_384_imagenet22k.h5) | 196.7M | 117.1G | 384 | 87.6 | |
## TinyNet
- [Keras TinyNet](keras_cv_attention_models/mobilenetv3_family#tinynet) includes implementation of [PDF 2010.14819 Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets](https://arxiv.org/pdf/2010.14819.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------- | ------ | ------- | ----- | -------- | ------------ |
| [TinyNetE](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/tinynet_e_imagenet.h5) | 2.04M | 25.22M | 106 | 59.86 | 2152.36 qps |
| [TinyNetD](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/tinynet_d_imagenet.h5) | 2.34M | 53.35M | 152 | 66.96 | 1905.56 qps |
| [TinyNetC](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/tinynet_c_imagenet.h5) | 2.46M | 103.22M | 184 | 71.23 | 1353.44 qps |
| [TinyNetB](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/tinynet_b_imagenet.h5) | 3.73M | 206.28M | 188 | 74.98 | 1196.06 qps |
| [TinyNetA](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mobilenetv3_family/tinynet_a_imagenet.h5) | 6.19M | 343.74M | 192 | 77.65 | 981.976 qps |
## TinyViT
- [Keras TinyViT](keras_cv_attention_models/tinyvit) includes implementation of [PDF 2207.10666 TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://arxiv.org/pdf/2207.10666.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------- | ------ | ----- | ----- | -------- | ------------ |
| [TinyViT_5M, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_5m_224_imagenet.h5) | 5.4M | 1.3G | 224 | 79.1 | 631.414 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_5m_224_imagenet21k-ft1k.h5) | 5.4M | 1.3G | 224 | 80.7 | 631.414 qps |
| [TinyViT_11M, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_11m_224_imagenet.h5) | 11M | 2.0G | 224 | 81.5 | 509.818 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_11m_224_imagenet21k-ft1k.h5) | 11M | 2.0G | 224 | 83.2 | 509.818 qps |
| [TinyViT_21M, distill](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_21m_224_imagenet.h5) | 21M | 4.3G | 224 | 83.1 | 410.676 qps |
| - [21k_ft1k](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_21m_224_imagenet21k-ft1k.h5) | 21M | 4.3G | 224 | 84.8 | 410.676 qps |
| - [21k_ft1k, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_21m_384_imagenet21k-ft1k.h5) | 21M | 13.8G | 384 | 86.2 | 199.458 qps |
| - [21k_ft1k, 512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/tinyvit/tiny_vit_21m_512_imagenet21k-ft1k.h5) | 21M | 27.0G | 512 | 86.5 | 122.846 qps |
## UniFormer
- [Keras UniFormer](keras_cv_attention_models/uniformer) includes implementation of [PDF 2201.09450 UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://arxiv.org/pdf/2201.09450.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| -------------------- | ------ | ------ | ----- | -------- | ------------ |
| [UniformerSmall32, token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_32_224_token_label.h5) | 22M | 3.66G | 224 | 83.4 | 577.334 qps |
| [UniformerSmall64](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_64_224_imagenet.h5) | 22M | 3.66G | 224 | 82.9 | 562.794 qps |
| - [token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_64_224_token_label.h5) | 22M | 3.66G | 224 | 83.4 | 562.794 qps |
| [UniformerSmallPlus32](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_plus_32_224_imagenet.h5) | 24M | 4.24G | 224 | 83.4 | 546.82 qps |
| - [token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_plus_32_224_token_label.h5) | 24M | 4.24G | 224 | 83.9 | 546.82 qps |
| [UniformerSmallPlus64](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_plus_64_224_imagenet.h5) | 24M | 4.23G | 224 | 83.4 | 538.193 qps |
| - [token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_small_plus_64_224_token_label.h5) | 24M | 4.23G | 224 | 83.6 | 538.193 qps |
| [UniformerBase32, token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_base_32_224_token_label.h5) | 50M | 8.32G | 224 | 85.1 | 272.485 qps |
| [UniformerBase64](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_base_64_224_imagenet.h5) | 50M | 8.31G | 224 | 83.8 | 286.963 qps |
| - [token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_base_64_224_token_label.h5) | 50M | 8.31G | 224 | 84.8 | 286.963 qps |
| [UniformerLarge64, token_label](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_large_64_224_token_label.h5) | 100M | 19.79G | 224 | 85.6 | 154.761 qps |
| - [token_label, 384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/uniformer/uniformer_large_64_384_token_label.h5) | 100M | 63.11G | 384 | 86.3 | 75.3487 qps |
## VanillaNet
- [Keras VanillaNet](keras_cv_attention_models/vanillanet) is for [PDF 2305.12972 VanillaNet: the Power of Minimalism in Deep Learning](https://arxiv.org/pdf/2305.12972.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------------- | ------ | ------ | ----- | -------- | ------------ |
| [VanillaNet5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_5_imagenet.h5) | 22.33M | 8.46G | 224 | 72.49 | 598.964 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_5_deploy_imagenet.h5) | 15.52M | 5.17G | 224 | 72.49 | 798.199 qps |
| [VanillaNet6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_6_imagenet.h5) | 56.12M | 10.11G | 224 | 76.36 | 465.031 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_6_deploy_imagenet.h5) | 32.51M | 6.00G | 224 | 76.36 | 655.944 qps |
| [VanillaNet7](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_7_imagenet.h5) | 56.67M | 11.84G | 224 | 77.98 | 375.479 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_7_deploy_imagenet.h5) | 32.80M | 6.90G | 224 | 77.98 | 527.723 qps |
| [VanillaNet8](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_8_imagenet.h5) | 65.18M | 13.50G | 224 | 79.13 | 341.157 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_8_deploy_imagenet.h5) | 37.10M | 7.75G | 224 | 79.13 | 479.328 qps |
| [VanillaNet9](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_9_imagenet.h5) | 73.68M | 15.17G | 224 | 79.87 | 312.815 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_9_deploy_imagenet.h5) | 41.40M | 8.59G | 224 | 79.87 | 443.464 qps |
| [VanillaNet10](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_10_imagenet.h5) | 82.19M | 16.83G | 224 | 80.57 | 277.871 qps |
| - [deploy=True](https://github.com/leondgarse/keras_cv_attention_models/releases/download/vanillanet/vanillanet_10_deploy_imagenet.h5) | 45.69M | 9.43G | 224 | 80.57 | 408.082 qps |
| VanillaNet11 | 90.69M | 18.49G | 224 | 81.08 | 267.026 qps |
| - deploy=True | 50.00M | 10.27G | 224 | 81.08 | 377.239 qps |
| VanillaNet12 | 99.20M | 20.16G | 224 | 81.55 | 229.987 qps |
| - deploy=True | 54.29M | 11.11G | 224 | 81.55 | 358.076 qps |
| VanillaNet13 | 107.7M | 21.82G | 224 | 82.05 | 218.256 qps |
| - deploy=True | 58.59M | 11.96G | 224 | 82.05 | 334.244 qps |
## VOLO
- [Keras VOLO](keras_cv_attention_models/volo) is for [PDF 2106.13112 VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/pdf/2106.13112.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| ------- | ------ | ------- | ----- | -------- | ------------ |
| [VOLO_d1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d1_224_imagenet.h5) | 27M | 4.82G | 224 | 84.2 | |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d1_384_imagenet.h5) | 27M | 14.22G | 384 | 85.2 | |
| [VOLO_d2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d2_224_imagenet.h5) | 59M | 9.78G | 224 | 85.2 | |
| - [384](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d2_384_imagenet.h5) | 59M | 28.84G | 384 | 86.0 | |
| [VOLO_d3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d3_224_imagenet.h5) | 86M | 13.80G | 224 | 85.4 | |
| - [448](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d3_448_imagenet.h5) | 86M | 55.50G | 448 | 86.3 | |
| [VOLO_d4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d4_224_imagenet.h5) | 193M | 29.39G | 224 | 85.7 | |
| - [448](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d4_448_imagenet.h5) | 193M | 117.81G | 448 | 86.8 | |
| [VOLO_d5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d5_224_imagenet.h5) | 296M | 53.34G | 224 | 86.1 | |
| - [448](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d5_448_imagenet.h5) | 296M | 213.72G | 448 | 87.0 | |
| - [512](https://github.com/leondgarse/keras_cv_attention_models/releases/download/volo/volo_d5_512_imagenet.h5) | 296M | 279.36G | 512 | 87.1 | |
## WaveMLP
- [Keras WaveMLP](keras_cv_attention_models/mlp_family#wavemlp) includes implementation of [PDF 2111.12294 An Image Patch is a Wave: Quantum Inspired Vision MLP](https://arxiv.org/pdf/2111.12294.pdf).
| Model | Params | FLOPs | Input | Top1 Acc | T4 Inference |
| --------- | ------ | ------ | ----- | -------- | ------------ |
| [WaveMLP_T](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/wavemlp_t_imagenet.h5) | 17M | 2.47G | 224 | 80.9 | 523.4 qps |
| [WaveMLP_S](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/wavemlp_s_imagenet.h5) | 30M | 4.55G | 224 | 82.9 | 203.445 qps |
| [WaveMLP_M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/mlp_family/wavemlp_m_imagenet.h5) | 44M | 7.92G | 224 | 83.3 | 147.155 qps |
| WaveMLP_B | 63M | 10.26G | 224 | 83.6 | |
***
# Detection Models
## EfficientDet
- [Keras EfficientDet](keras_cv_attention_models/efficientdet) includes implementation of [Paper 1911.09070 EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/pdf/1911.09070.pdf).
- `Det-AdvProp + AutoAugment` [Paper 2103.13886 Robust and Accurate Object Detection via Adversarial Learning](https://arxiv.org/pdf/2103.13886.pdf).
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| ------------------ | ------ | ------- | ----- | ----------- | ------- | ------------ |
| [EfficientDetD0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d0_512_coco.h5) | 3.9M | 2.55G | 512 | 34.3 | 34.6 | 248.009 qps |
| - Det-AdvProp | 3.9M | 2.55G | 512 | 35.1 | 35.3 | 248.009 qps |
| [EfficientDetD1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d1_640_coco.h5) | 6.6M | 6.13G | 640 | 40.2 | 40.5 | 133.139 qps |
| - Det-AdvProp | 6.6M | 6.13G | 640 | 40.8 | 40.9 | 133.139 qps |
| [EfficientDetD2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d2_768_coco.h5) | 8.1M | 11.03G | 768 | 43.5 | 43.9 | 89.0523 qps |
| - Det-AdvProp | 8.1M | 11.03G | 768 | 44.3 | 44.3 | 89.0523 qps |
| [EfficientDetD3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d3_896_coco.h5) | 12.0M | 24.95G | 896 | 46.8 | 47.2 | 50.0498 qps |
| - Det-AdvProp | 12.0M | 24.95G | 896 | 47.7 | 48.0 | 50.0498 qps |
| [EfficientDetD4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d4_1024_coco.h5) | 20.7M | 55.29G | 1024 | 49.3 | 49.7 | 28.0086 qps |
| - Det-AdvProp | 20.7M | 55.29G | 1024 | 50.4 | 50.4 | 28.0086 qps |
| [EfficientDetD5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d5_1280_coco.h5) | 33.7M | 135.62G | 1280 | 51.2 | 51.5 | |
| - Det-AdvProp | 33.7M | 135.62G | 1280 | 52.2 | 52.5 | |
| [EfficientDetD6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d6_1280_coco.h5) | 51.9M | 225.93G | 1280 | 52.1 | 52.6 | |
| [EfficientDetD7](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d7_1536_coco.h5) | 51.9M | 325.34G | 1536 | 53.4 | 53.7 | |
| [EfficientDetD7X](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_d7x_1536_coco.h5) | 77.0M | 410.87G | 1536 | 54.4 | 55.1 | |
| [EfficientDetLite0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite0_320_coco.h5) | 3.2M | 0.98G | 320 | 27.5 | 26.41 | 599.616 qps |
| [EfficientDetLite1](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite1_384_coco.h5) | 4.2M | 1.97G | 384 | 32.6 | 31.50 | 369.273 qps |
| [EfficientDetLite2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite2_448_coco.h5) | 5.3M | 3.38G | 448 | 36.2 | 35.06 | 278.263 qps |
| [EfficientDetLite3](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite3_512_coco.h5) | 8.4M | 7.50G | 512 | 39.9 | 38.77 | 180.871 qps |
| [EfficientDetLite3X](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite3x_640_coco.h5) | 9.3M | 14.01G | 640 | 44.0 | 42.64 | 115.271 qps |
| [EfficientDetLite4](https://github.com/leondgarse/keras_cv_attention_models/releases/download/efficientdet/efficientdet_lite4_640_coco.h5) | 15.1M | 20.20G | 640 | 44.4 | 43.18 | 95.4122 qps |
## YOLO_NAS
- [Keras YOLO_NAS](keras_cv_attention_models/yolov8) includes implementation of [Github Deci-AI/super-gradients](https://github.com/Deci-AI/super-gradients) YOLO-NAS models.
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| ----------------------- | ------ | ------ | ----- | ----------- | ------- | ------------ |
| [YOLO_NAS_S](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_s_before_reparam_coco.h5) | 12.88M | 16.96G | 640 | 47.5 | | 240.087 qps |
| - [use_reparam_conv=False](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_s_coco.h5) | 12.18M | 15.92G | 640 | 47.5 | | 345.595 qps |
| [YOLO_NAS_M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_m_before_reparam_coco.h5) | 33.86M | 47.12G | 640 | 51.55 | | 128.96 qps |
| - [use_reparam_conv=False](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_m_coco.h5) | 31.92M | 43.91G | 640 | 51.55 | | 167.935 qps |
| [YOLO_NAS_L](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_l_before_reparam_coco.h5) | 44.53M | 64.53G | 640 | 52.22 | | 98.6069 qps |
| - [use_reparam_conv=False](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolo_nas_l_coco.h5) | 42.02M | 59.95G | 640 | 52.22 | | 131.11 qps |
## YOLOR
- [Keras YOLOR](keras_cv_attention_models/yolor) includes implementation of [Paper 2105.04206 You Only Learn One Representation: Unified Network for Multiple Tasks](https://arxiv.org/pdf/2105.04206.pdf).
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| ---------- | ------ | ------- | ----- | ----------- | ------- | ------------ |
| [YOLOR_CSP](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_csp_coco.h5) | 52.9M | 60.25G | 640 | 50.0 | 52.8 | 118.746 qps |
| [YOLOR_CSPX](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_csp_x_coco.h5) | 99.8M | 111.11G | 640 | 51.5 | 54.8 | 67.9444 qps |
| [YOLOR_P6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_p6_coco.h5) | 37.3M | 162.87G | 1280 | 52.5 | 55.7 | 49.3128 qps |
| [YOLOR_W6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_w6_coco.h5) | 79.9M | 226.67G | 1280 | 53.6 ? | 56.9 | 40.2355 qps |
| [YOLOR_E6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_e6_coco.h5) | 115.9M | 341.62G | 1280 | 50.3 ? | 57.6 | 21.5719 qps |
| [YOLOR_D6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolor/yolor_d6_coco.h5) | 151.8M | 467.88G | 1280 | 50.8 ? | 58.2 | 16.6061 qps |
## YOLOV7
- [Keras YOLOV7](keras_cv_attention_models/yolov7) includes implementation of [Paper 2207.02696 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/pdf/2207.02696.pdf).
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| ----------- | ------ | ------ | ----- | ----------- | ------- | ------------ |
| [YOLOV7_Tiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_tiny_coco.h5) | 6.23M | 2.90G | 416 | 33.3 | | 845.903 qps |
| [YOLOV7_CSP](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_csp_coco.h5) | 37.67M | 53.0G | 640 | 51.4 | | 137.441 qps |
| [YOLOV7_X](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_x_coco.h5) | 71.41M | 95.0G | 640 | 53.1 | | 82.0534 qps |
| [YOLOV7_W6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_w6_coco.h5) | 70.49M | 180.1G | 1280 | 54.9 | | 49.9841 qps |
| [YOLOV7_E6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_e6_coco.h5) | 97.33M | 257.6G | 1280 | 56.0 | | 31.3852 qps |
| [YOLOV7_D6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_d6_coco.h5) | 133.9M | 351.4G | 1280 | 56.6 | | 26.1346 qps |
| [YOLOV7_E6E](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov7/yolov7_e6e_coco.h5) | 151.9M | 421.7G | 1280 | 56.8 | | 20.1331 qps |
## YOLOV8
- [Keras YOLOV8](keras_cv_attention_models/yolov8) includes implementation of [Github ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) detection and classification models.
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| --------- | ------ | ------ | ----- | ----------- | ------- | ------------ |
| [YOLOV8_N](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_n_coco.h5) | 3.16M | 4.39G | 640 | 37.3 | | 614.042 qps |
| [YOLOV8_S](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_s_coco.h5) | 11.17M | 14.33G | 640 | 44.9 | | 349.528 qps |
| [YOLOV8_M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_m_coco.h5) | 25.90M | 39.52G | 640 | 50.2 | | 160.212 qps |
| [YOLOV8_L](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_l_coco.h5) | 43.69M | 82.65G | 640 | 52.9 | | 104.452 qps |
| [YOLOV8_X](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_x_coco.h5) | 68.23M | 129.0G | 640 | 53.9 | | 66.0428 qps |
| [YOLOV8_X6](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_x6_coco.h5) | 97.42M | 522.6G | 1280 | 56.7 ? | | 17.4368 qps |
## YOLOX
- [Keras YOLOX](keras_cv_attention_models/yolox) includes implementation of [Paper 2107.08430 YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/pdf/2107.08430.pdf).
| Model | Params | FLOPs | Input | COCO val AP | test AP | T4 Inference |
| --------- | ------ | ------- | ----- | ----------- | ------- | ------------ |
| [YOLOXNano](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_nano_coco.h5) | 0.91M | 0.53G | 416 | 25.8 | | 930.57 qps |
| [YOLOXTiny](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_tiny_coco.h5) | 5.06M | 3.22G | 416 | 32.8 | | 745.2 qps |
| [YOLOXS](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_s_coco.h5) | 9.0M | 13.39G | 640 | 40.5 | 40.5 | 380.38 qps |
| [YOLOXM](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_m_coco.h5) | 25.3M | 36.84G | 640 | 46.9 | 47.2 | 181.084 qps |
| [YOLOXL](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_l_coco.h5) | 54.2M | 77.76G | 640 | 49.7 | 50.1 | 111.517 qps |
| [YOLOXX](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolox/yolox_x_coco.h5) | 99.1M | 140.87G | 640 | 51.5 | 51.5 | 62.3189 qps |
***
# Language Models
## GPT2
- [Keras GPT2](keras_cv_attention_models/gpt2) includes implementation of [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf). `T4 Inference` is tested using `input_shape=[1, 1024]`.
| Model | Params | FLOPs | vocab_size | LAMBADA PPL | T4 Inference |
| ---------------- | ------- | ------- | ---------- | ----------- | ------------ |
| [GPT2_Base](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpt2/gpt2_base_webtext.h5) | 163.04M | 146.42G | 50257 | 35.13 | 51.4483 qps |
| [GPT2_Medium](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpt2/gpt2_medium_webtext.h5) | 406.29M | 415.07G | 50257 | 15.60 | 21.756 qps |
| [GPT2_Large](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpt2/gpt2_large_webtext.h5) | 838.36M | 890.28G | 50257 | 10.87 | |
| [GPT2_XLarge](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpt2/gpt2_xlarge_webtext.1.h5), [+.2](https://github.com/leondgarse/keras_cv_attention_models/releases/download/gpt2/gpt2_xlarge_webtext.2.h5) | 1.638B | 1758.3G | 50257 | 8.63 | |
## LLaMA2
- [Keras LLaMA2](keras_cv_attention_models/llama2) includes implementation of [PDF 2307.09288 Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/pdf/2307.09288.pdf).
- `tiny_stories` weights ported from [Github karpathy/llama2.c](https://github.com/karpathy/llama2.c), and `LLaMA2_1B` model weights ported from [Github jzhang38/TinyLlama](https://githubfast.com/jzhang38/TinyLlama) `TinyLlama-1.1B-Chat-V0.4` one.
| Model | Params | FLOPs | vocab_size | Val loss | T4 Inference |
| ----------- | ------ | ------ | ---------- | -------- | ------------ |
| [LLaMA2_15M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/llama2/llama2_15m_tiny_stories.h5) | 24.41M | 4.06G | 32000 | 1.072 | |
| [LLaMA2_42M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/llama2/llama2_42m_tiny_stories.h5) | 58.17M | 50.7G | 32000 | 0.847 | |
| [LLaMA2_110M](https://github.com/leondgarse/keras_cv_attention_models/releases/download/llama2/llama2_110m_tiny_stories.h5) | 134.1M | 130.2G | 32000 | 0.760 | |
| [LLaMA2_1B](https://github.com/leondgarse/keras_cv_attention_models/releases/download/llama2/llama2_1b_tiny_llama_1.1B_chat_v0.4.h5) | 1.10B | 2.50T | 32003 | |
| LLaMA2_7B | 6.74B | 14.54T | 32000 | | |
***
# Stable Diffusion
- [Keras Stable Diffusion](keras_cv_attention_models/stable_diffusion) includes implementation of [PDF 2112.10752 High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/pdf/2112.10752.pdf). Weights ported from [Github runwayml/stable-diffusion](https://github.com/runwayml/stable-diffusion) `sd-v1-5.ckpt`.
| Model | Params | FLOPs | Input | Download |
| ------------------- | ------ | ------- | ------------------- | ------------------- |
| ViTTextLargePatch14 | 123.1M | 6.67G | [None, 77] | [vit_text_large_patch14_clip.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/beit/vit_text_large_patch14_clip.h5) |
| Encoder | 34.16M | 559.6G | [None, 512, 512, 3] | [encoder_v1_5.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/stable_diffusion/encoder_v1_5.h5) |
| UNet | 859.5M | 404.4G | [None, 64, 64, 4] | [unet_v1_5.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/stable_diffusion/unet_v1_5.h5) |
| Decoder | 49.49M | 1259.5G | [None, 64, 64, 4] | [decoder_v1_5.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/stable_diffusion/decoder_v1_5.h5) |
***
# Segmentation Models
## YOLOV8 Segmentation
- [Keras YOLOV8](keras_cv_attention_models/yolov8) includes implementation of [Github ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) segmentation models.
| Model | Params | FLOPs | Input | COCO val mask AP | T4 Inference |
| ------------ | ------ | ------- | ----- | ---------------- | ------------ |
| [YOLOV8_N_SEG](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_n_seg_coco.h5) | 3.41M | 6.02G | 640 | 30.5 | |
| [YOLOV8_S_SEG](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_s_seg_coco.h5) | 11.82M | 20.08G | 640 | 36.8 | |
| [YOLOV8_M_SEG](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_m_seg_coco.h5) | 27.29M | 52.33G | 640 | 40.8 | |
| [YOLOV8_L_SEG](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_l_seg_coco.h5) | 46.00M | 105.29G | 640 | 42.6 | |
| [YOLOV8_X_SEG](https://github.com/leondgarse/keras_cv_attention_models/releases/download/yolov8/yolov8_x_seg_coco.h5) | 71.83M | 164.30G | 640 | 43.4 | |
## Segment Anything
- [Keras Segment Anything](keras_cv_attention_models/segment_anything) includes implementation of [PDF 2304.02643 Segment Anything](https://arxiv.org/abs/2304.02643).
| Model | Params | FLOPs | Input | COCO val mask AP | T4 Inference |
| ------------------- | ------ | ----- | ----- | ---------------- | ------------ |
| [MobileSAM](https://github.com/leondgarse/keras_cv_attention_models/releases/download/segment_anything/mobile_sam_5m_image_encoder_1024_sam.h5) | 5.74M | 39.4G | 1024 | 41.0 | |
| [TinySAM](https://github.com/leondgarse/keras_cv_attention_models/releases/download/segment_anything/tinysam_5m_image_encoder_1024_sam.h5) | 5.74M | 39.4G | 1024 | 41.9 | |
| [EfficientViT_SAM_L0](https://github.com/leondgarse/keras_cv_attention_models/releases/download/segment_anything/efficientvit_sam_l0_image_encoder_1024_sam.h5) | 30.73M | 35.4G | 512 | 45.7 | |
***
# Licenses
- This part is copied and modified according to [Github rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models).
- **Code**. The code here is licensed MIT. It is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue. So far all of the pretrained weights available here are pretrained on ImageNet and COCO with a select few that have some additional pretraining.
- **ImageNet Pretrained Weights**. ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.
- **COCO Pretrained Weights**. Should follow [cocodataset termsofuse](https://cocodataset.org/#termsofuse). The annotations in COCO dataset belong to the COCO Consortium and are licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). The COCO Consortium does not own the copyright of the images. Use of the images must abide by the [Flickr Terms of Use](https://www.flickr.com/creativecommons/). The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.
- **Pretrained on more than ImageNet and COCO**. Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.
***
# Citing
- **BibTeX**
```bibtex
@misc{leondgarse,
author = {Leondgarse},
title = {Keras CV Attention Models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.6506947},
howpublished = {\url{https://github.com/leondgarse/keras_cv_attention_models}}
}
```
- **Latest DOI**: [](https://zenodo.org/badge/latestdoi/391777965)
***