Image Classification
timm
PyTorch
rdnet
DongHyunKim commited on
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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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+
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+ A RDNet image classification model. Trained on ImageNet-1k, original torchvision weights.
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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+
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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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+
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+ print(o.shape)
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+ ```
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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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+
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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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+
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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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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
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+
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+ # or equivalently (without needing to set num_classes=0)
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+
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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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+
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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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+
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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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+ ```