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timm/
repvit_m0_9.dist_300e_in1k

Name: repvit_m0_9.dist_300e_in1k
Rating: 2.5 (5086 reviews)

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Model card for repvit_m0_9.dist_300e_in1k

A RepViT image classification model. Trained on ImageNet-1k with distillation by paper authors.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 5.5
- GMACs: 0.8
- Activations (M): 7.4
- Image size: 224 x 224
Papers:
- RepViT: Revisiting Mobile CNN From ViT Perspective: https://arxiv.org/abs/2307.09283
Original: https://github.com/THU-MIG/RepViT
Dataset: ImageNet-1k

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('repvit_m0_9.dist_300e_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(
    'repvit_m0_9.dist_300e_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, 48, 56, 56])
    #  torch.Size([1, 96, 28, 28])
    #  torch.Size([1, 192, 14, 14])
    #  torch.Size([1, 384, 7, 7])

    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(
    'repvit_m0_9.dist_300e_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, 384, 7, 7) shaped tensor

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

Citation

bibtex

@misc{wang2023repvit,
  title={RepViT: Revisiting Mobile CNN From ViT Perspective}, 
  author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
  year={2023},
  eprint={2307.09283},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

timm/repvit_m0_9.dist_300e_in1k

Model card for repvit_m0_9.dist_300e_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Citation

timm/
repvit_m0_9.dist_300e_in1k