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Add closest EfficientNet variants #1967

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Add closest EfficientNet variants #1967

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0271f9a
add closest variants
pkgoogle Oct 31, 2024
cb752d0
added to presets file
pkgoogle Nov 4, 2024
63420d6
format pass
pkgoogle Nov 4, 2024
319bcf1
fix quote literal and formatting
pkgoogle Nov 4, 2024
a844601
fix uris
pkgoogle Nov 4, 2024
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100 changes: 99 additions & 1 deletion keras_hub/src/models/efficientnet/efficientnet_presets.py
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Expand Up @@ -14,10 +14,25 @@
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b0_ra_imagenet",
},
"efficientnet_b0_ra4_e3600_r224_imagenet": {
"metadata": {
"description": (
"EfficientNet B0 model pre-trained on the ImageNet 1k dataset by"
" Ross Wightman. Trained with timm scripts using hyper-parameters"
" inspired by the MobileNet-V4 small, mixed with go-to hparams "
'from timm and "ResNet Strikes Back".'
),
"params": 5288548,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b0_ra4_e3600_r224_imagenet",
},
"efficientnet_b1_ft_imagenet": {
"metadata": {
"description": (
"EfficientNet B1 model fine-trained on the ImageNet 1k dataset."
"EfficientNet B1 model pre-trained on the ImageNet 1k dataset."
),
"params": 7794184,
"official_name": "EfficientNet",
Expand All @@ -26,4 +41,87 @@
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b1_ft_imagenet",
},
"efficientnet_b1_ra4_e3600_r240_imagenet": {
"metadata": {
"description": (
"EfficientNet B1 model pre-trained on the ImageNet 1k dataset by"
" Ross Wightman. Trained with timm scripts using hyper-parameters"
" inspired by the MobileNet-V4 small, mixed with go-to hparams "
'from timm and "ResNet Strikes Back".'
),
"params": 7794184,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b1_ra4_e3600_r240_imagenet",
},
"efficientnet_b2_ra_imagenet": {
"metadata": {
"description": (
"EfficientNet B2 model pre-trained on the ImageNet 1k dataset "
"with RandAugment recipe."
),
"params": 9109994,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b2_ra_imagenet",
},
"efficientnet_b3_ra2_imagenet": {
"metadata": {
"description": (
"EfficientNet B3 model pre-trained on the ImageNet 1k dataset "
"with RandAugment2 recipe."
),
"params": 12233232,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b3_ra2_imagenet",
},
"efficientnet_b4_ra2_imagenet": {
"metadata": {
"description": (
"EfficientNet B4 model pre-trained on the ImageNet 1k dataset "
"with RandAugment2 recipe."
),
"params": 19341616,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b4_ra2_imagenet",
},
"efficientnet_b5_sw_imagenet": {
"metadata": {
"description": (
"EfficientNet B5 model pre-trained on the ImageNet 12k dataset "
"by Ross Wightman. Based on Swin Transformer train / pretrain "
"recipe with modifications (related to both DeiT and ConvNeXt recipes)."
),
"params": 30389784,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b5_sw_imagenet",
},
"efficientnet_b5_sw_ft_imagenet": {
"metadata": {
"description": (
"EfficientNet B5 model pre-trained on the ImageNet 12k dataset "
"and fine-tuned on ImageNet-1k by Ross Wightman. Based on Swin "
"Transformer train / pretrain recipe with modifications "
"(related to both DeiT and ConvNeXt recipes)."
),
"params": 30389784,
"official_name": "EfficientNet",
"path": "efficientnet",
"model_card": "https://arxiv.org/abs/1905.11946",
},
"kaggle_handle": "kaggle://keras/efficientnet/keras/efficientnet_b5_sw_ft_imagenet",
},
}
16 changes: 16 additions & 0 deletions keras_hub/src/utils/timm/convert_efficientnet.py
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Expand Up @@ -18,6 +18,22 @@
"width_coefficient": 1.0,
"depth_coefficient": 1.1,
},
"b2": {
"width_coefficient": 1.1,
"depth_coefficient": 1.2,
},
"b3": {
"width_coefficient": 1.2,
"depth_coefficient": 1.4,
},
"b4": {
"width_coefficient": 1.4,
"depth_coefficient": 1.8,
},
"b5": {
"width_coefficient": 1.6,
"depth_coefficient": 2.2,
},
}


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25 changes: 23 additions & 2 deletions tools/checkpoint_conversion/convert_efficientnet_checkpoints.py
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Expand Up @@ -2,9 +2,23 @@
Convert efficientnet checkpoints.

python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b0_ra_imagenet --upload_uri kaggle://kerashub/efficientnet/keras/efficientnet_b0_ra_imagenet
--preset efficientnet_b0_ra_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b0_ra_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b1_ft_imagenet --upload_uri kaggle://kerashub/efficientnet/keras/efficientnet_b1_ft_imagenet
--preset efficientnet_b0_ra4_e3600_r224_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b0_ra4_e3600_r224_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b1_ft_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b1_ft_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b1_ra4_e3600_r240_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b1_ra4_e3600_r240_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b2_ra_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b2_ra_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b3_ra2_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b3_ra2_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b4_ra2_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b4_ra2_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b5_sw_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b5_sw_imagenet
python tools/checkpoint_conversion/convert_efficientnet_checkpoints.py \
--preset efficientnet_b5_sw_ft_imagenet --upload_uri kaggle://keras/efficientnet/keras/efficientnet_b5_sw_ft_imagenet
"""

import os
Expand All @@ -22,7 +36,14 @@

PRESET_MAP = {
"efficientnet_b0_ra_imagenet": "timm/efficientnet_b0.ra_in1k",
"efficientnet_b0_ra4_e3600_r224_imagenet": "timm/efficientnet_b0.ra4_e3600_r224_in1k",
"efficientnet_b1_ft_imagenet": "timm/efficientnet_b1.ft_in1k",
"efficientnet_b1_ra4_e3600_r240_imagenet": "timm/efficientnet_b1.ra4_e3600_r240_in1k",
"efficientnet_b2_ra_imagenet": "timm/efficientnet_b2.ra_in1k",
"efficientnet_b3_ra2_imagenet": "timm/efficientnet_b3.ra2_in1k",
"efficientnet_b4_ra2_imagenet": "timm/efficientnet_b4.ra2_in1k",
"efficientnet_b5_sw_imagenet": "timm/efficientnet_b5.sw_in12k",
"efficientnet_b5_sw_ft_imagenet": "timm/efficientnet_b5.sw_in12k_ft_in1k",
}
FLAGS = flags.FLAGS

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