Convnext-tf - Unofficial tensorflow keras implementation of ConvNeXt

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Deep Learningconvnext-tf
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

    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
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