timm / repvgg_a2.rvgg_in1k

huggingface.co
Total runs: 23.5K
24-hour runs: 0
7-day runs: 1.1K
30-day runs: 9.6K
Model's Last Updated: January 21 2025
image-classification

Introduction of repvgg_a2.rvgg_in1k

Model Details of repvgg_a2.rvgg_in1k

Model card for repvgg_a2.rvgg_in1k

A RepVGG image classification model. Trained on ImageNet-1k by paper authors.

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 Usage
Image Classification
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('repvgg_a2.rvgg_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
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(
    'repvgg_a2.rvgg_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, 64, 112, 112])
    #  torch.Size([1, 96, 56, 56])
    #  torch.Size([1, 192, 28, 28])
    #  torch.Size([1, 384, 14, 14])
    #  torch.Size([1, 1408, 7, 7])

    print(o.shape)
Image Embeddings
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(
    'repvgg_a2.rvgg_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, 1408, 7, 7) 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
@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}}
}
@inproceedings{ding2021repvgg,
  title={Repvgg: Making vgg-style convnets great again},
  author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13733--13742},
  year={2021}
}

Runs of timm repvgg_a2.rvgg_in1k on huggingface.co

23.5K
Total runs
0
24-hour runs
1.7K
3-day runs
1.1K
7-day runs
9.6K
30-day runs

More Information About repvgg_a2.rvgg_in1k huggingface.co Model

More repvgg_a2.rvgg_in1k license Visit here:

https://choosealicense.com/licenses/mit

repvgg_a2.rvgg_in1k huggingface.co

repvgg_a2.rvgg_in1k huggingface.co is an AI model on huggingface.co that provides repvgg_a2.rvgg_in1k's model effect (), which can be used instantly with this timm repvgg_a2.rvgg_in1k model. huggingface.co supports a free trial of the repvgg_a2.rvgg_in1k model, and also provides paid use of the repvgg_a2.rvgg_in1k. Support call repvgg_a2.rvgg_in1k model through api, including Node.js, Python, http.

repvgg_a2.rvgg_in1k huggingface.co Url

https://huggingface.co/timm/repvgg_a2.rvgg_in1k

timm repvgg_a2.rvgg_in1k online free

repvgg_a2.rvgg_in1k huggingface.co is an online trial and call api platform, which integrates repvgg_a2.rvgg_in1k's modeling effects, including api services, and provides a free online trial of repvgg_a2.rvgg_in1k, you can try repvgg_a2.rvgg_in1k online for free by clicking the link below.

timm repvgg_a2.rvgg_in1k online free url in huggingface.co:

https://huggingface.co/timm/repvgg_a2.rvgg_in1k

repvgg_a2.rvgg_in1k install

repvgg_a2.rvgg_in1k is an open source model from GitHub that offers a free installation service, and any user can find repvgg_a2.rvgg_in1k on GitHub to install. At the same time, huggingface.co provides the effect of repvgg_a2.rvgg_in1k install, users can directly use repvgg_a2.rvgg_in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

repvgg_a2.rvgg_in1k install url in huggingface.co:

https://huggingface.co/timm/repvgg_a2.rvgg_in1k

Url of repvgg_a2.rvgg_in1k

repvgg_a2.rvgg_in1k huggingface.co Url

Provider of repvgg_a2.rvgg_in1k huggingface.co

timm
ORGANIZATIONS

Other API from timm

huggingface.co

Total runs: 19.0M
Run Growth: -1.8M
Growth Rate: -9.67%
Updated: January 21 2025
huggingface.co

Total runs: 4.9M
Run Growth: 3.3M
Growth Rate: 68.60%
Updated: January 21 2025
huggingface.co

Total runs: 125.5K
Run Growth: 103.6K
Growth Rate: 82.60%
Updated: October 25 2023
huggingface.co

Total runs: 121.9K
Run Growth: -120.0K
Growth Rate: -98.99%
Updated: January 21 2025
huggingface.co

Total runs: 113.9K
Run Growth: -112.3K
Growth Rate: -100.13%
Updated: January 21 2025
huggingface.co

Total runs: 112.5K
Run Growth: 101.7K
Growth Rate: 89.79%
Updated: January 21 2025
huggingface.co

Total runs: 90.3K
Run Growth: 64.4K
Growth Rate: 80.38%
Updated: January 21 2025
huggingface.co

Total runs: 31.4K
Run Growth: 8.7K
Growth Rate: 27.99%
Updated: January 21 2025
huggingface.co

Total runs: 24.0K
Run Growth: 7.5K
Growth Rate: 31.41%
Updated: January 21 2025
huggingface.co

Total runs: 22.4K
Run Growth: 15.1K
Growth Rate: 70.96%
Updated: January 21 2025
huggingface.co

Total runs: 20.8K
Run Growth: 6.5K
Growth Rate: 32.11%
Updated: January 21 2025
huggingface.co

Total runs: 20.5K
Run Growth: 6.0K
Growth Rate: 30.02%
Updated: January 21 2025
huggingface.co

Total runs: 20.5K
Run Growth: 5.8K
Growth Rate: 28.79%
Updated: January 21 2025
huggingface.co

Total runs: 20.2K
Run Growth: 15.2K
Growth Rate: 76.29%
Updated: January 21 2025