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Description:

kimm 0.2.2

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
2024/05/29:

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=[...])
kimm.utils.get_reparameterized_model
kimm.export.export_tflite
kimm.export.export_onnx

import keras
import kimm
import numpy as np

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

# Initialize model with pretrained ImageNet weights
x = keras.random.uniform([1, 224, 224, 3])
model = kimm.models.MobileOneS0()
y = model.predict(x)
print(y.shape)
# (1, 1000)

# 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-5
)

# 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)
# kimm.export.export_onnx(reparameterized_model, 224, "model.onnx")

# 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
model = kimm.models.MobileOneS0(
feature_extractor=True, feature_keys=["BLOCK2_S16", "BLOCK3_S32"]
)
features = model.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)
# TOP (1, 1000)

Installation
pip install keras kimm -U

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

ConvMixer
ICLR 2022 Submission
timm
kimm.models.ConvMixer*

ConvNeXt
CVPR 2022
timm
kimm.models.ConvNeXt*

DenseNet
CVPR 2017
timm
kimm.models.DenseNet*

EfficientNet
ICML 2019
timm
kimm.models.EfficientNet*

EfficientNetLite
ICML 2019
timm
kimm.models.EfficientNetLite*

EfficientNetV2
ICML 2021
timm
kimm.models.EfficientNetV2*

GhostNet
CVPR 2020
timm
kimm.models.GhostNet*

GhostNetV2
NeurIPS 2022
timm
kimm.models.GhostNetV2*

GhostNetV3
arXiv 2024
github
kimm.models.GhostNetV3*

HGNet

timm
kimm.models.HGNet*

HGNetV2

timm
kimm.models.HGNetV2*

InceptionNeXt
arXiv 2023
timm
kimm.models.InceptionNeXt*

InceptionV3
CVPR 2016
timm
kimm.models.InceptionV3

LCNet
arXiv 2021
timm
kimm.models.LCNet*

MobileNetV2
CVPR 2018
timm
kimm.models.MobileNetV2*

MobileNetV3
ICCV 2019
timm
kimm.models.MobileNetV3*

MobileOne
CVPR 2023
timm
kimm.models.MobileOne*

MobileViT
ICLR 2022
timm
kimm.models.MobileViT*

MobileViTV2
arXiv 2022
timm
kimm.models.MobileViTV2*

RegNet
CVPR 2020
timm
kimm.models.RegNet*

RepVGG
CVPR 2021
timm
kimm.models.RepVGG*

ResNet
CVPR 2015
timm
kimm.models.ResNet*

TinyNet
NeurIPS 2020
timm
kimm.models.TinyNet*

VGG
ICLR 2015
timm
kimm.models.VGG*

ViT
ICLR 2021
timm
kimm.models.VisionTransformer*

Xception
CVPR 2017
keras
kimm.models.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}}
}