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https://github.com/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training
SimpleAICV:pytorch training and testing examples.
https://github.com/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training
centernet darknet deeplabv3plus dino fcos kd mae pytorch regnetx repvgg resnet retinanet sam segment-anything solov2 ttfnet u2net vit yolact
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SimpleAICV:pytorch training and testing examples.
- Host: GitHub
- URL: https://github.com/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training
- Owner: zgcr
- License: mit
- Created: 2020-05-31T05:37:26.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-05-18T02:30:56.000Z (5 months ago)
- Last Synced: 2024-05-22T07:52:56.004Z (5 months ago)
- Topics: centernet, darknet, deeplabv3plus, dino, fcos, kd, mae, pytorch, regnetx, repvgg, resnet, retinanet, sam, segment-anything, solov2, ttfnet, u2net, vit, yolact
- Language: Jupyter Notebook
- Homepage:
- Size: 67 MB
- Stars: 403
- Watchers: 9
- Forks: 95
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - zgcr/SimpleAICV-pytorch-ImageNet-COCO-training - pytorch-ImageNet-COCO-training?style=social"/> : SimpleAICV:pytorch training example on ImageNet(ILSVRC2012)/COCO2017/VOC2007+2012 datasets.Include ResNet/DarkNet/RetinaNet/FCOS/CenterNet/TTFNet/YOLOv3/YOLOv4/YOLOv5/YOLOX. (Applications)
- awesome-yolo-object-detection - zgcr/SimpleAICV-pytorch-ImageNet-COCO-training - pytorch-ImageNet-COCO-training?style=social"/> : SimpleAICV:pytorch training example on ImageNet(ILSVRC2012)/COCO2017/VOC2007+2012 datasets.Include ResNet/DarkNet/RetinaNet/FCOS/CenterNet/TTFNet/YOLOv3/YOLOv4/YOLOv5/YOLOX. (Applications)
README
- [My column](#my-column)
- [📢 News!](#-news)
- [Introduction](#introduction)
- [All task training results](#all-task-training-results)
- [Environments](#environments)
- [Download my pretrained models and experiments records](#download-my-pretrained-models-and-experiments-records)
- [Prepare datasets](#prepare-datasets)
- [CIFAR10](#cifar10)
- [CIFAR100](#cifar100)
- [ImageNet 1K(ILSVRC2012)](#imagenet-1kilsvrc2012)
- [ImageNet 21K(Winter 2021 release)](#imagenet-21kwinter-2021-release)
- [ACCV2022](#accv2022)
- [VOC2007 and VOC2012](#voc2007-and-voc2012)
- [COCO2017](#coco2017)
- [SAMACOCO](#samacoco)
- [Objects365(v2,2020)](#objects365v22020)
- [ADE20K](#ade20k)
- [CelebA-HQ](#celeba-hq)
- [FFHQ](#ffhq)
- [Places365-standard/challenge](#places365-standardchallenge)
- [How to train and test model](#how-to-train-and-test-model)
- [How to use gradio demo](#how-to-use-gradio-demo)
- [Reference](#reference)
- [Citation](#citation)
# My column
https://www.zhihu.com/column/c_1692623656205897728
# 📢 News!
* 2024/04/15: support segment-anything model training/testing/jupyter example/gradio demo.
# Introduction
**This repository provides simple training and testing examples for the following tasks:**
| task | support dataset | support network |
| ----------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------------- |
| Image classification task | CIFAR100
ImageNet1K(ILSVRC2012)
ImageNet21K(Winter 2021 release)
ACCV2022 | ResNet
DarkNet
RepVGG
RegNetX
ViT
VAN |
| Object detection task | VOC2007 and VOC2012
COCO2017
Objects365(v2,2020) | RetinaNet
FCOS
CenterNet
TTFNet
DETR
DINO-DETR |
| Semantic segmentation task | ADE20K | DeepLabv3+
U2Net |
| Instance segmentation task | COCO2017 | YOLACT
SOLOv2 |
| Knowledge distillation task | ImageNet1K(ILSVRC2012) | KD loss(for ResNet)
DML loss(for ResNet) |
| Contrastive learning task | ImageNet1K(ILSVRC2012) | DINO(for ResNet) |
| Masked image modeling task | ImageNet1K(ILSVRC2012)
ACCV2022 | MAE(for ViT) |
| OCR text detection task | / | DBNet |
| OCR text recognition task | / | CTC Model |
| Human matting task | / | PFAN Matting model |
| Salient object detection task | / | PFAN Segmentation model |
| Face detection task | / | RetinaFace |
| Interactive segmentation task | / | SAM(segment-anything) |
| Image inpainting task | CelebA-HQ
Places365-standard
Places365-challenge | AOT-GAN
TRANSX-LKA-AOT-GAN |
| Diffusion model task | CIFAR10
CIFAR100
CelebA-HQ
FFHQ | DDPM
DDIM |
# All task training results
**See all task training results in [results.md](results.md).**
# Environments
**1、This repository only supports running on ubuntu(verison>=18.04 LTS).**
**2、This repository only support one node one gpu/one node multi gpus mode with pytorch DDP training.**
**3、Please make sure your Python environment version>=3.7.**
**4、Please make sure your pytorch version>=1.10.**
**5、If you want to use torch.complie() function,please make sure your pytorch version>=2.0.Using pytorch2.0/2.2/2.3,don't use pytorch2.1.**
**Use pip or conda to install those Packages in your Python environment:**
```
torch
torchvision
pillow
numpy
Cython
colormath
pycocotools
opencv-python
scipy
einops
scikit-image
pyclipper
shapely
imagesize
nltk
tqdm
yapf
onnx
onnxruntime
onnxsim
thop
gradio==4.26.0
transformers==4.41.2
open-clip-torch==2.24.0
```
**If you want to use xformers,install xformers Packge from offical github repository:**
https://github.com/facebookresearch/xformers
**If you want to use dino-detr model,install MultiScaleDeformableAttention Packge in your Python environment:**
cd to simpleAICV/detection/compile_multiscale_deformable_attention,then run commands:
```
chmod +x make.sh
./make.sh
```
# Download my pretrained models and experiments records
You can download all my pretrained models and all my experiments records/checkpoints from huggingface or Baidu-Netdisk.
If you only want to download all my pretrained models(model.state_dict()),you can download pretrained_models folder.
```
# huggingface
https://huggingface.co/zgcr654321/classification_training/tree/main
https://huggingface.co/zgcr654321/contrastive_learning_training/tree/main
https://huggingface.co/zgcr654321/detection_training/tree/main
https://huggingface.co/zgcr654321/image_inpainting_training/tree/main
https://huggingface.co/zgcr654321/diffusion_model_training/tree/main
https://huggingface.co/zgcr654321/distillation_training/tree/main
https://huggingface.co/zgcr654321/instance_segmentation_training/tree/main
https://huggingface.co/zgcr654321/masked_image_modeling_training/tree/main
https://huggingface.co/zgcr654321/ocr_text_detection_training/tree/main
https://huggingface.co/zgcr654321/ocr_text_recognition_training/tree/main
https://huggingface.co/zgcr654321/human_matting_training/tree/main
https://huggingface.co/zgcr654321/salient_object_detection_training/tree/main
https://huggingface.co/zgcr654321/face_detection_training/tree/main
https://huggingface.co/zgcr654321/interactive_segmentation_training/tree/main
https://huggingface.co/zgcr654321/semantic_segmentation_training/tree/main
https://huggingface.co/zgcr654321/pretrained_models/tree/main
# Baidu-Netdisk
链接：https://pan.baidu.com/s/1yhEwaZhrb2NZRpJ5eEqHBw
提取码：rgdo
```
# Prepare datasets
## CIFAR10
Make sure the folder architecture as follows:
```
CIFAR10
|
|-----batches.meta unzip from cifar-10-python.tar.gz
|-----data_batch_1 unzip from cifar-10-python.tar.gz
|-----data_batch_2 unzip from cifar-10-python.tar.gz
|-----data_batch_3 unzip from cifar-10-python.tar.gz
|-----data_batch_4 unzip from cifar-10-python.tar.gz
|-----data_batch_5 unzip from cifar-10-python.tar.gz
|-----readme.html unzip from cifar-10-python.tar.gz
|-----test_batch unzip from cifar-10-python.tar.gz
```
## CIFAR100
Make sure the folder architecture as follows:
```
CIFAR100
|
|-----train unzip from cifar-100-python.tar.gz
|-----test unzip from cifar-100-python.tar.gz
|-----meta unzip from cifar-100-python.tar.gz
```
## ImageNet 1K(ILSVRC2012)
Make sure the folder architecture as follows:
```
ILSVRC2012
|
|-----train----1000 sub classes folders
|-----val------1000 sub classes folders
Please make sure the same class has same class folder name in train and val folders.
```
## ImageNet 21K(Winter 2021 release)
Make sure the folder architecture as follows:
```
ImageNet21K
|
|-----train-----------10450 sub classes folders
|-----val-------------10450 sub classes folders
|-----small_classes---10450 sub classes folders
|-----imagenet21k_miil_tree.pth
Please make sure the same class has same class folder name in train and val folders.
```
## ACCV2022
Make sure the folder architecture as follows:
```
ACCV2022
|
|-----train-------------5000 sub classes folders
|-----testa-------------60000 images
|-----accv2022_broken_list.json
```
## VOC2007 and VOC2012
Make sure the folder architecture as follows:
```
VOCdataset
| |----Annotations
| |----ImageSets
|----VOC2007------|----JPEGImages
| |----SegmentationClass
| |----SegmentationObject
|
| |----Annotations
| |----ImageSets
|----VOC2012------|----JPEGImages
| |----SegmentationClass
| |----SegmentationObject
```
## COCO2017
Make sure the folder architecture as follows:
```
COCO2017
| |----captions_train2017.json
| |----captions_val2017.json
|--annotations---|----instances_train2017.json
| |----instances_val2017.json
| |----person_keypoints_train2017.json
| |----person_keypoints_val2017.json
|
| |----train2017
|----images------|----val2017
```
## SAMACOCO
Make sure the folder architecture as follows:
```
SAMA-COCO
| |----sama_coco_train.json
| |----sama_coco_validation.json
|--annotations---|----train_labels.json
| |----validation_labels.json
| |----test_labels.json
| |----image_info_test2017.json
| |----image_info_test-dev2017.json
|
| |----train
|----images------|----validation
```
## Objects365(v2,2020)
Make sure the folder architecture as follows:
```
objects365_2020
|
| |----zhiyuan_objv2_train.json
|--annotations---|----zhiyuan_objv2_val.json
| |----sample_2020.json
|
| |----train all train patch folders
|----images------|----val all val patch folders
|----test all test patch folders
```
## ADE20K
Make sure the folder architecture as follows:
```
ADE20K
| |----training
|---images--------|----validation
| |----testing
|
| |----training
|---annotations---|----validation
```
## CelebA-HQ
Make sure the folder architecture as follows:
```
CelebA-HQ
| |----female
|---train---------|----male
|
| |----female
|---val-----------|----male
```
## FFHQ
Make sure the folder architecture as follows:
```
FFHQ
|
|---images
|---ffhq-dataset-v1.json
|---ffhq-dataset-v2.json
```
## Places365-standard/challenge
Make sure the folder architecture as follows:
```
Places365-standard/challenge
|
| |---train_large all sub folders
|---high_resolution_images---|---val_large all images
| |---test_large all images
```
# How to train and test model
**If you want to train or test model,you need enter a training experiment folder directory,then run train.sh or test.sh.**
For example,you can enter in folder classification_training/imagenet/resnet50.
If you want to restart train this model,please delete checkpoints and log folders first,then run train.sh:
```
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/train_classification_model.py --work-dir ./
```
if you want to test this model,you need have a pretrained model first,modify trained_model_path in test_config.py,then run test.sh:
```
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/test_classification_model.py --work-dir ./
```
**CUDA_VISIBLE_DEVICES is used to specify the gpu ids for this training.Please make sure the number of nproc_per_node equal to the number of using gpu cards.Make sure master_addr/master_port are unique for each training.**
**All checkpoints/log are saved in your executing training/testing experiment folder directory.**
**Also, You can modify super parameters in train_config.py/test_config.py.**
# How to use gradio demo
cd to gradio_demo,we have:
```
classification demo
detection demo
semantic_segmentation demo
instance_segmentation demo
text_detection demo
text_recognition demo
human_matting demo
salient_object_detection demo
face_detection demo
segment_anything demo
```
For example,you can run detection gradio demo(please prepare trained model weight first):
```
python gradio_detect_single_image.py
```
# Reference
https://github.com/facebookresearch/segment-anything
# Citation
If you find my work useful in your research, please consider citing:
```
@inproceedings{zgcr,
title={SimpleAICV-pytorch-training-examples},
author={zgcr},
year={2020-2024}
}
```