Delete rdnet_base.nv_1k.md

rdnet_base.nv_1k.md

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----
-tags:
-- image-classification
-- timm
-- rdnet
-library_name: timm
-datasets:
-- imagenet-1k
----
-# Model card for rdnet_base.nv_in1k
-
-A RDNet image classification model. Trained on ImageNet-1k, original torchvision weights.
-
-## Model Details
-- **Model Type:** Image classification / feature backbone
-- **Model Stats:**
-  - Imagenet-1k validation top-1 accuracy: 84.4%
-  - Params (M): 87
-  - GMACs: 15.4
-  - Image size: 224 x 224
-- **Papers:**
-  - DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs: https://arxiv.org/abs/2403.19588
-- **Dataset:** ImageNet-1k
-
-## Model Usage
-### Image Classification
-```python
-from urllib.request import urlopen
-from PIL import Image
-import timm
-import torch
-
-img = Image.open(urlopen(
-    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
-))
-
-model = timm.create_model('rdnet_base.nv_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(
-    'rdnet_base.nv_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, 224, 224])
-    #  torch.Size([1, 128, 112, 112])
-    #  torch.Size([1, 256, 56, 56])
-    #  torch.Size([1, 512, 28, 28])
-    #  torch.Size([1, 512, 14, 14])
-    #  torch.Size([1, 512, 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(
-    'rdnet_base.nv_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, 512, 7, 7) shaped tensor
-
-output = model.forward_head(output, pre_logits=True)
-# output is a (1, num_features) shaped tensor
-```
-
-### Citation
-```
-@misc{kim2024densenets,
-    title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs}, 
-    author={Donghyun Kim and Byeongho Heo and Dongyoon Han},
-    year={2024},
-    eprint={2403.19588},
-    archivePrefix={arXiv},
-}
-```