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Create README.md
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README.md
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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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---
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# Model card for rdnet_base.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: 84.4%
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- Params (M): 87
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- GMACs: 15.4
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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_base.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_base.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, 64, 224, 224])
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# torch.Size([1, 128, 112, 112])
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# torch.Size([1, 256, 56, 56])
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# torch.Size([1, 512, 28, 28])
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# torch.Size([1, 512, 14, 14])
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# torch.Size([1, 512, 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_base.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, 512, 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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```
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