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https://github.com/james77777778/keras-aug
A library that includes pure TF/Keras preprocessing and augmentation layers, providing support for various data types such as images, labels, bounding boxes, segmentation masks, and more.
https://github.com/james77777778/keras-aug
augmentation keras keras-cv preprocessing tensorflow
Last synced: 4 months ago
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A library that includes pure TF/Keras preprocessing and augmentation layers, providing support for various data types such as images, labels, bounding boxes, segmentation masks, and more.
- Host: GitHub
- URL: https://github.com/james77777778/keras-aug
- Owner: james77777778
- License: apache-2.0
- Created: 2023-04-10T14:36:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-13T12:25:40.000Z (about 1 year ago)
- Last Synced: 2024-04-25T20:20:40.085Z (9 months ago)
- Topics: augmentation, keras, keras-cv, preprocessing, tensorflow
- Language: Python
- Homepage: https://kerasaug.readthedocs.io/en/latest/
- Size: 672 KB
- Stars: 8
- Watchers: 3
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# KerasAug
[](https://github.com/james77777778/keras-aug/actions/workflows/actions.yml?query=branch%3Amain++)
[](https://codecov.io/gh/james77777778/keras-aug)
[](https://pypi.org/project/keras-aug/)
[](https://huggingface.co/spaces/james77777778/KerasAug)
## Description
KerasAug is a library that includes Keras 3 preprocessing and augmentation layers, providing support for various data types such as images, labels, bounding boxes, segmentation masks, and more.
> [!NOTE]
> See `docs/*.py` for the GIF generation. YOLOV8-like pipeline for bounding boxes and segmentation masks.
KerasAug aims to provide fast, robust and user-friendly preprocessing and augmentation layers, facilitating seamless integration with Keras 3 and `tf.data`.
The APIs largely follow `torchvision`, and the correctness of the layers has been verified through unit tests.
Also, you can check out the demo app on HF:
App here: [](https://huggingface.co/spaces/james77777778/KerasAug)
## Why KerasAug
- 🚀 Supports many preprocessing & augmentation layers across all backends (JAX, TensorFlow and Torch).
- 🧰 Seamlessly integrates with `tf.data`, offering a performant and scalable data pipeline.
- 🔥 Follows the same API design as `torchvision`.
- 🙌 Depends only on Keras 3.
## Installation
```bash
pip install keras keras-aug -U
```
> [!IMPORTANT]
> Make sure you have installed a supported backend for Keras.
## Quickstart
### Rock, Paper and Scissors Image Classification
[](https://colab.research.google.com/drive/11xc0nW06iWQ_R-oH4wLB_MYV4GY4mNwy?usp=sharing)
```python
import keras
import tensorflow as tf
import tensorflow_datasets as tfds
from keras_aug import layers as ka_layers
BATCH_SIZE = 64
NUM_CLASSES = 3
INPUT_SIZE = (128, 128)
# Create a `tf.data.Dataset`-compatible preprocessing pipeline.
# Note that this example works with all backends.
train_dataset, validation_dataset = tfds.load(
"rock_paper_scissors", as_supervised=True, split=["train", "test"]
)
train_dataset = (
train_dataset.batch(BATCH_SIZE)
.map(
lambda images, labels: {
"images": tf.cast(images, "float32") / 255.0,
"labels": tf.one_hot(labels, NUM_CLASSES),
}
)
.map(ka_layers.vision.Resize(INPUT_SIZE))
.shuffle(128)
.map(ka_layers.vision.RandAugment())
.map(ka_layers.vision.CutMix(num_classes=NUM_CLASSES))
.map(ka_layers.vision.Rescale(scale=2.0, offset=-1)) # [0, 1] to [-1, 1]
.map(lambda data: (data["images"], data["labels"]))
.prefetch(tf.data.AUTOTUNE)
)
validation_dataset = (
validation_dataset.batch(BATCH_SIZE)
.map(
lambda images, labels: {
"images": tf.cast(images, "float32") / 255.0,
"labels": tf.one_hot(labels, NUM_CLASSES),
}
)
.map(ka_layers.vision.Resize(INPUT_SIZE))
.map(ka_layers.vision.Rescale(scale=2.0, offset=-1)) # [0, 1] to [-1, 1]
.map(lambda data: (data["images"], data["labels"]))
.prefetch(tf.data.AUTOTUNE)
)
# Create a model using MobileNetV2 as the backbone.
backbone = keras.applications.MobileNetV2(
input_shape=(*INPUT_SIZE, 3), include_top=False
)
backbone.trainable = False
inputs = keras.Input((*INPUT_SIZE, 3))
x = backbone(inputs)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(NUM_CLASSES, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.summary()
model.compile(
loss="categorical_crossentropy",
optimizer=keras.optimizers.SGD(learning_rate=1e-3, momentum=0.9),
metrics=["accuracy"],
)
# Train and evaluate your model
model.fit(train_dataset, validation_data=validation_dataset, epochs=8)
model.evaluate(validation_dataset)
```
The above example runs with all backends (JAX, TensorFlow, Torch).
### More Examples
- [YOLOV8 object detection pipeline](guides/voc_yolov8_aug.py) [](https://colab.research.google.com/drive/1AgnnvfTRMHKq--7gvmHP7RyxTeQResV4?usp=sharing)
- [YOLOV8 semantic segmentation pipeline](guides/oxford_yolov8_aug.py) [](https://colab.research.google.com/drive/1IJwUPiHreO7iIJ3VewgfLRoBdFdcQcJE?usp=sharing)
## Gradio App
```bash
gradio deploy
```
## Citing KerasAug
```bibtex
@misc{chiu2023kerasaug,
title={KerasAug},
author={Hongyu, Chiu},
year={2023},
howpublished={\url{https://github.com/james77777778/keras-aug}},
}
```