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timm/
eca_resnet33ts.ra2_in1k

Name: eca_resnet33ts.ra2_in1k
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Model card for eca_resnet33ts.ra2_in1k

A ECA-ResNet image classification model (ResNet with 'Efficient Channel Attention'). This model features a tiered 3-layer stem without pooling and SiLU activations. Trained on ImageNet-1k by Ross Wightman in timm.

This model architecture is implemented using timm's flexible BYOBNet (Bring-Your-Own-Blocks Network).

BYOBNet allows configuration of:

block / stage layout
stem layout
output stride (dilation)
activation and norm layers
channel and spatial / self-attention layers

...and also includes timm features common to many other architectures, including:

stochastic depth
gradient checkpointing
layer-wise LR decay
per-stage feature extraction

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 19.7
- GMACs: 4.8
- Activations (M): 11.7
- Image size: train = 256 x 256, test = 288 x 288
Papers:
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks: https://arxiv.org/abs/1910.03151
- @: a
Dataset: ImageNet-1k
Original: https://github.com/huggingface/pytorch-image-models

Model Usage

Image Classification

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('eca_resnet33ts.ra2_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Feature Map Extraction

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'eca_resnet33ts.ra2_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 32, 128, 128])
    #  torch.Size([1, 256, 64, 64])
    #  torch.Size([1, 512, 32, 32])
    #  torch.Size([1, 1536, 16, 16])
    #  torch.Size([1, 1280, 8, 8])

    print(o.shape)

Image Embeddings

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'eca_resnet33ts.ra2_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 8, 8) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

Citation

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/huggingface/pytorch-image-models}}
}

bibtex

@InProceedings{wang2020eca,
  title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
  author={Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo and Qinghua Hu},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

r

timm/eca_resnet33ts.ra2_in1k

Model card for eca_resnet33ts.ra2_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

Citation

timm/
eca_resnet33ts.ra2_in1k