Keras Image Models

• Latest Updates
• Introduction
• Usage
• Installation
• Quickstart
  • Image classification with ImageNet weights
  • An end-to-end fine-tuning example: cats vs. dogs dataset
  • Grad-CAM
• Model Zoo
• License
• Acknowledgements

Latest Updates

2024/06/02:

• Add docstrings for all kimm models.
• Merge reparameterizable layers into 1 ReparameterizableConv2D
• Add GhostNetV3* from huawei-noah/Efficient-AI-Backbones

Introduction

Keras Image Models (kimm) is a collection of image models, blocks and layers written in Keras 3. The goal is to offer SOTA models with pretrained weights in a user-friendly manner.

KIMM is:

• 🚀 A model zoo where almost all models come with pre-trained weights on ImageNet.
• 🧰 Providing APIs to export models to .tflite and .onnx.
• 🔧 Supporting the reparameterization technique.
• ✨ Integrated with feature extraction capability.

Usage

• kimm.list_models
• kimm.models.*.available_feature_keys
• kimm.models.*(...)
• kimm.models.*(..., feature_extractor=True, feature_keys=[...])

import keras
import kimm

# List available models
print(kimm.list_models("mobileone", weights="imagenet"))
# ['MobileOneS0', 'MobileOneS1', 'MobileOneS2', 'MobileOneS3']

# Initialize model with pretrained ImageNet weights
# Note: all `kimm` models expect inputs in the value range of [0, 255] by
# default if `incldue_preprocessing=True`
x = keras.random.uniform([1, 224, 224, 3]) * 255.0
model = kimm.models.MobileOneS0()
y = model.predict(x)
print(y.shape)
# (1, 1000)

# Print some basic information about the model
print(model)
# <MobileOneS0 name=MobileOneS0, input_shape=(None, None, None, 3),
# default_size=224, preprocessing_mode="imagenet", feature_extractor=False,
# feature_keys=None>
# This information can also be accessed through properties
print(model.input_shape, model.default_size, model.preprocessing_mode)

# List available feature keys of the model class
print(kimm.models.MobileOneS0.available_feature_keys)
# ['STEM_S2', 'BLOCK0_S4', 'BLOCK1_S8', 'BLOCK2_S16', 'BLOCK3_S32']

# Enable feature extraction by setting `feature_extractor=True`
# `feature_keys` can be optionally specified
feature_extractor = kimm.models.MobileOneS0(
    feature_extractor=True, feature_keys=["BLOCK2_S16", "BLOCK3_S32"]
)
features = feature_extractor.predict(x)
for feature_name, feature in features.items():
    print(feature_name, feature.shape)
# BLOCK2_S16 (1, 14, 14, 256), BLOCK3_S32 (1, 7, 7, 1024), ...

Note

All models in kimm expect inputs in the value range of [0, 255] by default if incldue_preprocessing=True. Some models only accept static inputs. You should explicitly specify the input shape for these models by input_shape=[*, *, 3].

Advanced Usage

• kimm.utils.get_reparameterized_model
• kimm.export.export_tflite
• kimm.export.export_onnx

import keras
import kimm
import numpy as np

# Initialize a reparameterizable model
x = keras.random.uniform([1, 224, 224, 3]) * 255.0
model = kimm.models.MobileOneS0()
y = model.predict(x)

# Get reparameterized model by kimm.utils.get_reparameterized_model
reparameterized_model = kimm.utils.get_reparameterized_model(model)
y2 = reparameterized_model.predict(x)
np.testing.assert_allclose(
    keras.ops.convert_to_numpy(y), keras.ops.convert_to_numpy(y2), atol=1e-3
)

# Export model to tflite format
kimm.export.export_tflite(reparameterized_model, 224, "model.tflite")

# Export model to onnx format
# Note: must be "channels_first" format before the exporting
# kimm.export.export_onnx(reparameterized_model, 224, "model.onnx")

Installation

pip install keras kimm -U

Important

Make sure you have installed a supported backend for Keras.

Quickstart

Image classification using the model pretrained on ImageNet

Using kimm.models.VisionTransformerTiny16:

1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step
Predicted: [('n02504458', 'African_elephant', 0.6895825), ('n01871265', 'tusker', 0.17934209), ('n02504013', 'Indian_elephant', 0.12927249)]

An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset

Using kimm.models.EfficientNetLiteB0:

Reference: Transfer learning & fine-tuning (keras.io)

Grad-CAM

Using kimm.models.MobileViTS:

Reference: Grad-CAM class activation visualization (keras.io)

Model Zoo

Model	Paper	Weights are ported from	API (kimm.models.*)
ConvMixer	ICLR 2022 Submission	timm	ConvMixer*
ConvNeXt	CVPR 2022	timm	ConvNeXt*
DenseNet	CVPR 2017	timm	DenseNet*
EfficientNet	ICML 2019	timm	EfficientNet*
EfficientNetLite	ICML 2019	timm	EfficientNetLite*
EfficientNetV2	ICML 2021	timm	EfficientNetV2*
GhostNet	CVPR 2020	timm	GhostNet*
GhostNetV2	NeurIPS 2022	timm	GhostNetV2*
GhostNetV3	arXiv 2024	github	GhostNetV3*
HGNet		timm	HGNet*
HGNetV2		timm	HGNetV2*
InceptionNeXt	CVPR 2024	timm	InceptionNeXt*
InceptionV3	CVPR 2016	timm	InceptionV3
LCNet	arXiv 2021	timm	LCNet*
MobileNetV2	CVPR 2018	timm	MobileNetV2*
MobileNetV3	ICCV 2019	timm	MobileNetV3*
MobileOne	CVPR 2023	timm	MobileOne*
MobileViT	ICLR 2022	timm	MobileViT*
MobileViTV2	arXiv 2022	timm	MobileViTV2*
RegNet	CVPR 2020	timm	RegNet*
RepVGG	CVPR 2021	timm	RepVGG*
ResNet	CVPR 2015	timm	ResNet*
TinyNet	NeurIPS 2020	timm	TinyNet*
VGG	ICLR 2015	timm	VGG*
ViT	ICLR 2021	timm	VisionTransformer*
Xception	CVPR 2017	keras	Xception

The export scripts can be found in tools/convert_*.py.

License

Please refer to timm as this project is built upon it.

kimm Code

The code here is licensed Apache 2.0.

Acknowledgements

Thanks for these awesome projects that were used in kimm

• https://github.com/keras-team/keras
• https://github.com/huggingface/pytorch-image-models

Citing

BibTeX

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

@misc{hy2024kimm,
  author = {Hongyu Chiu},
  title = {Keras Image Models},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/james77777778/kimm}}
}