Convnext-tf - Unofficial tensorflow keras implementation of ConvNeXt

Last update: Oct 06, 2022

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Overview

ConvNeXt Tensorflow

This is unofficial tensorflow keras implementation of ConvNeXt.
Its based on official PyTorch implementation.

Pre-trained Models

name	resolution	pretrain	[email protected]	#params	FLOPs	model
`convnext_tiny_224`	224x224	ImageNet-1K	82.1	28M	4.5G	github
`convnext_small_224`	224x224	ImageNet-1K	83.1	50M	8.7G	github
`convnext_base_224`	224x224	ImageNet-21K-1K	85.8	89M	15.4G	github
`convnext_base_384`	384x384	ImageNet-21K-1K	86.8	89M	45.0G	github
`convnext_large_224`	224x224	ImageNet-21K-1K	86.6	198M	34.4G	github
`convnext_large_384`	384x384	ImageNet-21K-1K	87.5	198M	101.0G	github
`convnext_xlarge_224`	224x224	ImageNet-21K-1K	87.0	350M	60.9G	github
`convnext_xlarge_384`	384x384	ImageNet-21K-1K	87.8	350M	179.0G	github

Note

I've ported only ImageNet-21K-1K weights for base, large and xlarge models.
If you want to convert another pretrained weight in official repo, you can refer to this script or just let me know.

Examples

import tensorflow as tf
from models.convnext_tf import create_model

x = tf.zeros((1, 224, 224, 3), dtype=tf.float32)

model = create_model('convnext_tiny_224', input_shape=(224, 224), pretrained=True)
out = model(x) # (1, 1000)

model = create_model('convnext_tiny_224', input_shape=(224, 224), num_classes=1, pretrained=True)
out = model(x) # (1, 1)

model = create_model('convnext_tiny_224', input_shape=(224, 224), include_top=False, pretrained=True)
out = model(x) # (1, 16, 16, 768)

Reference

https://github.com/facebookresearch/ConvNeXt
https://github.com/rishigami/Swin-Transformer-TF

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Comments

Low accuracy on ImageNet1K validation

I'm using your TF-converted model (tiny) and testing on ImageNet 1K validation images, but I'm getting 78% accuracy instead of the officially published 82%.

Do you think that a 4 percent drop could be because of Torch/Tensorflow difference? Another converted model (https://github.com/sayakpaul/ConvNeXt-TF) attains 81% and the only difference between you and sayakpaul is the implementation of depthwise convolution. Have you tested your model on validation and if so, can you share the steps so that I could figure out the problem?

opened by app2scale 2

Releases(v0.1)

v0.1(Jan 12, 2022)

Source code(tar.gz)
Source code(zip)
convnext_base_22k_1k_224.h5(338.30 MB)
convnext_base_22k_1k_384.h5(338.31 MB)
convnext_large_22k_1k_224.h5(754.79 MB)
convnext_large_22k_1k_384.h5(754.78 MB)
convnext_small_1k_224_ema.h5(191.98 MB)
convnext_tiny_1k_224_ema.h5(109.27 MB)
convnext_xlarge_22k_1k_224_ema.h5(1336.26 MB)
convnext_xlarge_22k_1k_384_ema.h5(1336.26 MB)

Owner

GitHub Repository

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