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https://github.com/sebastian-sz/efficientnet-v2-keras
Efficientnet V2 adapted to Keras functional API.
https://github.com/sebastian-sz/efficientnet-v2-keras
keras keras-application onnx onnxruntime tensorflow tensorflow-lite tensorflow-models
Last synced: 4 months ago
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Efficientnet V2 adapted to Keras functional API.
- Host: GitHub
- URL: https://github.com/sebastian-sz/efficientnet-v2-keras
- Owner: sebastian-sz
- License: apache-2.0
- Created: 2021-07-30T13:23:56.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-23T01:58:34.000Z (almost 2 years ago)
- Last Synced: 2024-09-27T21:04:41.086Z (4 months ago)
- Topics: keras, keras-application, onnx, onnxruntime, tensorflow, tensorflow-lite, tensorflow-models
- Language: Python
- Homepage:
- Size: 255 KB
- Stars: 21
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
EfficientNetV2 models rewritten in Keras functional API.
### Changelog:
* Feb 2022:
* As of 2.8 Tensorflow release, the models in this repository (apart from XL variant) are accessible through `keras.applications.efficientnet_v2`
You are free to use this repo or Keras directly.
* Nov 2021:
* added more weights variants from original repo.
* added option to manually get preprocessing layer.
* Sept. 2021 - Added XL model variant.
* Changed layer naming convention.
* Re-exported weights.
# Table of contens
1. [Introduction](https://github.com/sebastian-sz/efficientnet-v2-keras#introduction)
2. [Quickstart](https://github.com/sebastian-sz/efficientnet-v2-keras#quickstart)
3. [Installation](https://github.com/sebastian-sz/efficientnet-v2-keras#installation)
4. [How to use](https://github.com/sebastian-sz/efficientnet-v2-keras#how-to-use)
5. [Original Weights](https://github.com/sebastian-sz/efficientnet-v2-keras#original-weights)
# Introduction
This is a package with EfficientNetV2 model variants adapted to Keras functional API.
I rewrote them this way so that the usage is similar to `keras.applications`.
The model's weights are converted from [original repository](https://github.com/google/automl/tree/master/efficientnetv2).
# Quickstart
You can use these models, similar to `keras.applications`:
```python
# Install
!pip install git+https://github.com/sebastian-sz/efficientnet-v2-keras@main
# Import package:
from efficientnet_v2 import EfficientNetV2S
import tensorflow as tf
# Use model directly:
model = EfficientNetV2S(
weights='imagenet', input_shape=(384, 384, 3)
)
model.summary()
# Or to extract features / fine tune:
backbone = EfficientNetV2S(
weights='imagenet',
input_shape=(384, 384, 3),
include_top=False
)
model = tf.keras.Sequential([
backbone,
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10) # 10 = num classes
])
model.compile(...)
model.fit(...)
```
You can fine tune these models, just like other Keras models.
For end-to-end fine-tuning and conversion examples check out the
[Colab Notebook](https://colab.research.google.com/drive/1CPTho02wBl48oOMqR2Wkj0xd90F3I9Uj?usp=sharing).
# Installation
There are multiple ways to install.
The only requirements are Tensorflow 2.2+ and Python 3.6+.
(Though, it is recommended to use **at least** Tensorflow 2.4)
### Option A: (recommended) pip install from github
`pip install git+https://github.com/sebastian-sz/efficientnet-v2-keras@main`
### Option B: Build from source
```bash
git clone https://github.com/sebastian-sz/efficientnet-v2-keras.git
cd efficientnet-v2-keras
pip install .
```
### Option C: (alternatively) no install:
If you do not want to install you could just drop the `efficientnet_v2/` directory, directly into your project.
### Option D: Docker
You can also install this package as an extension to official Tensorflow docker container:
Build: `docker build -t efficientnet_v2_keras .`
Run: `docker run -it --rm efficientnet_v2_keras`
For GPU support or different TAG you can (for example) pass
`--build-arg IMAGE_TAG=2.5.0-gpu`
in build command.
### Verify installation
If all goes well you should be able to import:
`from efficientnet_v2 import *`
# How to use
### Pretrained weights
Weights converted from original repository will be automatically downloaded, once you
pass `weights="imagenet"` (or `imagenet-21k`, `imagenet-21k-ft1k`) upon model creation.
There are 3 weight variants:
* `imagenet` - pretrained on Imagenet1k
* `imagenet-21k` - pretrained on Imagenet21k
* `imagenet-21k-ft1k` - pretrained on Imagenet21k and fine tuned on Imagenet1k
Note: `imagenet` weights have not been released for `XL` variant.
### Input shapes
The variants expect the following input shapes.
| Model variant | Input shape |
|:-------------:|:-----------:|
| B0 | `224,224` |
| B1 | `240,240` |
| B2 | `260,260` |
| B3 | `300,300` |
| S | `384,384` |
| M | `480,480` |
| L | `480,480` |
| XL | `512,512` |
### Preprocessing
##### Option A: preprocessing function
The preprocessing is different for `Bx` and `S/M/L/XL` variants.
`Bx`'s expect image normalized with Imagenet mean and stddev, while other's a simple
rescale:
```python
import tensorflow as tf
# Bx preprocessing:
def preprocess(image): # input image is in range 0-255.
mean_rgb = [0.485 * 255, 0.456 * 255, 0.406 * 255]
stddev_rgb = [0.229 * 255, 0.224 * 255, 0.225 * 255]
image -= tf.constant(mean_rgb, shape=(1, 1, 3), dtype=image.dtype)
image /= tf.constant(stddev_rgb, shape=(1, 1, 3), dtype=image.dtype)
return image
# S/M/L/XL preprocessing
def preprocess(image):
return (tf.cast(image, dtype=tf.float32) - 128.00) / 128.00
```
##### Option B: Preprocessing layers
or you can use [Preprocessing Layer](https://www.tensorflow.org/guide/keras/preprocessing_layers)
included in this repo:
```python
from efficientnet_v2 import get_preprocessing_layer
preprocessing_layer = get_preprocessing_layer(variant="b0")
```
### Fine-tuning
For fine-tuning example, check out the [Colab Notebook](https://colab.research.google.com/drive/1CPTho02wBl48oOMqR2Wkj0xd90F3I9Uj?usp=sharing).
### Tensorflow Lite
The models are TFLite compatible. You can convert them like any other Keras model:
```python
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open("efficientnet_lite.tflite", "wb") as file:
file.write(tflite_model)
```
### ONNX
The models are ONNX compatible. For ONNX Conversion you can use
[tf2onnx](https://github.com/onnx/tensorflow-onnx) package:
```python
!pip install tf2onnx==1.8.4
# Save the model in TF's Saved Model format:
model.save("my_saved_model/")
# Convert:
!python -m tf2onnx.convert \
--saved-model my_saved_model/ \
--output efficientnet_v2.onnx
```
# Original Weights
The original weights are present in the
[original repoistory](https://github.com/google/automl/tree/master/efficientnetv2).
The original models were also trained using Keras are compatible with TF 2.
### (Optionally) Convert the weights
The converted weights are on this repository's GitHub. If, for some reason, you wish to
download and convert original weights yourself, I prepared the utility scripts:
1. `bash scripts/download_all.sh`
2. `bash scripts/convert_all.sh`
# Bibliography
[1] [Original repository](https://github.com/google/automl/tree/master/efficientnetv2)
# Closing words
If you found this repo useful, please consider giving it a star!