convnext

Keras CovNeXt

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Summary

• CovNeXt article: PDF 2201.03545 A ConvNet for the 2020s. Model weights reloaded from Github facebookresearch/ConvNeXt.
• CovNeXtV2 article: PDF 2301.00808 ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. Model weights reloaded from Github facebookresearch/ConvNeXt-V2.

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Models

Model	Params	FLOPs	Input	Top1 Acc	Download
ConvNeXtTiny	28M	4.49G	224	82.1	tiny_imagenet.h5
- ImageNet21k-ft1k	28M	4.49G	224	82.9	tiny_224_21k.h5
- ImageNet21k-ft1k	28M	13.19G	384	84.1	tiny_384_21k.h5
ConvNeXtSmall	50M	8.73G	224	83.1	small_imagenet.h5
- ImageNet21k-ft1k	50M	8.73G	224	84.6	small_224_21k.h5
- ImageNet21k-ft1k	50M	25.67G	384	85.8	small_384_21k.h5
ConvNeXtBase	89M	15.42G	224	83.8	base_224_imagenet.h5
ConvNeXtBase	89M	45.32G	384	85.1	base_384_imagenet.h5
- ImageNet21k-ft1k	89M	15.42G	224	85.8	base_224_21k.h5
- ImageNet21k-ft1k	89M	45.32G	384	86.8	base_384_21k.h5
ConvNeXtLarge	198M	34.46G	224	84.3	large_224_imagenet.h5
ConvNeXtLarge	198M	101.28G	384	85.5	large_384_imagenet.h5
- ImageNet21k-ft1k	198M	34.46G	224	86.6	large_224_21k.h5
- ImageNet21k-ft1k	198M	101.28G	384	87.5	large_384_21k.h5
ConvNeXtXLarge, 21k	350M	61.06G	224	87.0	xlarge_224_21k.h5
ConvNeXtXLarge, 21k	350M	179.43G	384	87.8	xlarge_384_21k.h5
ConvNeXtXXLarge, clip	846M	198.09G	256	88.6	xxlarge_384_clip.h5

• ConvNeXtV2 models Note: ConvNeXtV2Huge weights are in float16 format, as float32 ones are too large that exceed 2GB.

Model	Params	FLOPs	Input	Top1 Acc	Download
ConvNeXtV2Atto	3.7M	0.55G	224	76.7	v2_atto_imagenet.h5
ConvNeXtV2Femto	5.2M	0.78G	224	78.5	v2_femto_imagenet.h5
ConvNeXtV2Pico	9.1M	1.37G	224	80.3	v2_pico_imagenet.h5
ConvNeXtV2Nano	15.6M	2.45G	224	81.9	v2_nano_imagenet.h5
- ImageNet21k-ft1k	15.6M	2.45G	224	82.1	v2_nano_224_21k.h5
- ImageNet21k-ft1k	15.6M	7.21G	384	83.4	v2_nano_384_21k.h5
ConvNeXtV2Tiny	28.6M	4.47G	224	83.0	v2_tiny_imagenet.h5
- ImageNet21k-ft1k	28.6M	4.47G	224	83.9	v2_tiny_224_21k.h5
- ImageNet21k-ft1k	28.6M	13.1G	384	85.1	v2_tiny_384_21k.h5
ConvNeXtV2Base	89M	15.4G	224	84.9	v2_base_imagenet.h5
- ImageNet21k-ft1k	89M	15.4G	224	86.8	v2_base_224_21k.h5
- ImageNet21k-ft1k	89M	45.2G	384	87.7	v2_base_224_21k.h5
ConvNeXtV2Large	198M	34.4G	224	85.8	v2_large_imagenet.h5
- ImageNet21k-ft1k	198M	34.4G	224	87.3	v2_large_224_21k.h5
- ImageNet21k-ft1k	198M	101.1G	384	88.2	v2_large_384_21k.h5
ConvNeXtV2Huge	660M	115G	224	86.3	v2_huge_imagenet.h5
- ImageNet21k-ft1k	660M	337.9G	384	88.7	v2_huge_384_21k.h5
- ImageNet21k-ft1k	660M	600.8G	512	88.9	v2_huge_512_21k.h5

Usage

from keras_cv_attention_models import convnext
mm = convnext.ConvNeXtBase()
# >>>> Load pretrained from: ~/.keras/models/convnext_base_224_imagenet.h5

# Run prediction
import tensorflow as tf
from tensorflow import keras
from skimage.data import chelsea
imm = keras.applications.imagenet_utils.preprocess_input(chelsea(), mode='torch') # Chelsea the cat
pred = mm(tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)).numpy()
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.9021103), ('n02123159', 'tiger_cat', 0.017981088), ...]

Change input resolution

from keras_cv_attention_models import convnext
mm = convnext.ConvNeXtV2Nano(input_shape=(480, 480, 3), pretrained='imagenet21k-ft1k')
# >>>> Load pretrained from: ~/.keras/models/convnext_v2_nano_384_imagenet21k-ft1k.h5

from skimage.data import chelsea
preds = mm(mm.preprocess_input(chelsea()))
print(mm.decode_predictions(preds)[0])
# [('n02124075', 'Egyptian_cat', 0.7427755), ('n02123159', 'tiger_cat', 0.092012934), ...]

Use dynamic input resolution by set input_shape=(None, None, 3).

from keras_cv_attention_models import convnext
model = convnext.ConvNeXtBase(input_shape=(None, None, 3), num_classes=0)

print(model(np.ones([1, 223, 123, 3])).shape)
# (1, 6, 3, 1024)
print(model(np.ones([1, 32, 526, 3])).shape)
# (1, 1, 16, 1024)

Verification with PyTorch version

inputs = np.random.uniform(size=(1, 224, 224, 3)).astype("float32")

""" PyTorch convnext_base """
sys.path.append('../ConvNeXt/')
import torch
from models import convnext as torch_convnext
torch_model = torch_convnext.convnext_base(pretrained=True)
_ = torch_model.eval()
torch_out = torch_model(torch.from_numpy(inputs).permute(0, 3, 1, 2)).detach().numpy()

""" Keras ConvNeXtBase """
from keras_cv_attention_models import convnext
mm = convnext.ConvNeXtBase(pretrained="imagenet", classifier_activation=None)
keras_out = mm(inputs).numpy()

""" Verification """
print(f"{np.allclose(torch_out, keras_out, atol=1e-3) = }")
# np.allclose(torch_out, keras_out, atol=1e-3) = True

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