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--- |
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datasets: |
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- imagenet-1k |
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library_name: timm |
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tags: |
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- image-classification |
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- timm |
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- rdnet |
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license: bsd-3-clause |
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--- |
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# Model card for rdnet_large.nv_in1k |
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A RDNet image classification model. Trained on ImageNet-1k, original torchvision weights. |
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## Model Details |
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Imagenet-1k validation top-1 accuracy: 84.8% |
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- Params (M): 186 |
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- GMACs: 34.7 |
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- Image size: 224 x 224 |
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- **Papers:** |
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- DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs: https://arxiv.org/abs/2403.19588 |
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- **Dataset:** ImageNet-1k |
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## Model Usage |
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### Image Classification |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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import torch |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model('rdnet_large.nv_in1k', pretrained=True) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) |
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``` |
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### Feature Map Extraction |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'rdnet_large.nv_in1k', |
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pretrained=True, |
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features_only=True, |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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for o in output: |
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# print shape of each feature map in output |
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# e.g.: |
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# torch.Size([1, 528, 56, 56]) |
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# torch.Size([1, 840, 28, 28]) |
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# torch.Size([1, 1528, 14, 14]) |
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# torch.Size([1, 2000, 7, 7]) |
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print(o.shape) |
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``` |
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### Image Embeddings |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'rdnet_large.nv_in1k', |
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pretrained=True, |
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num_classes=0, # remove classifier nn.Linear |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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# or equivalently (without needing to set num_classes=0) |
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output = model.forward_features(transforms(img).unsqueeze(0)) |
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# output is unpooled, a (1, 2000, 7, 7) shaped tensor |
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output = model.forward_head(output, pre_logits=True) |
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# output is a (1, num_features) shaped tensor |
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``` |
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### Citation |
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``` |
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@misc{kim2024densenets, |
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title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs}, |
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author={Donghyun Kim and Byeongho Heo and Dongyoon Han}, |
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year={2024}, |
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eprint={2403.19588}, |
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archivePrefix={arXiv}, |
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} |
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``` |