Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+- imagenet-21k-p
+---
+# Model card for tresnet_m.miil_in21k_ft_in1k
+
+A TResNet image classification model. Pretrained on ImageNet-21K-P ("ImageNet-21K Pretraining for the Masses", a 11k subset of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors.
+
+The weights for this model have been remapped and modified from the originals to work with standard BatchNorm instead of InplaceABN. `inplace_abn` can be problematic to build recently and ends up slower with `memory_format=channels_last`, torch.compile(), etc.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 31.4
+  - GMACs: 5.8
+  - Activations (M): 7.3
+  - Image size: 224 x 224
+- **Papers:**
+  - TResNet: High Performance GPU-Dedicated Architecture: https://arxiv.org/abs/2003.13630
+  - ImageNet-21K Pretraining for the Masses: https://arxiv.org/abs/2104.10972
+- **Dataset:** ImageNet-1k
+- **Pretrain Dataset:** ImageNet-21K-P
+- **Original:**
+  - https://github.com/Alibaba-MIIL/TResNet
+  - https://github.com/Alibaba-MIIL/ImageNet21K
+
+## 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('tresnet_m.miil_in21k_ft_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(
+    'tresnet_m.miil_in21k_ft_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, 56, 56])
+    #  torch.Size([1, 128, 28, 28])
+    #  torch.Size([1, 1024, 14, 14])
+    #  torch.Size([1, 2048, 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(
+    'tresnet_m.miil_in21k_ft_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, 2048, 7, 7) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+```bibtex
+@misc{ridnik2020tresnet,
+    title={TResNet: High Performance GPU-Dedicated Architecture},
+    author={Tal Ridnik and Hussam Lawen and Asaf Noy and Itamar Friedman},
+    year={2020},
+    eprint={2003.13630},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+```bibtex
+@misc{ridnik2021imagenet21k,
+  title={ImageNet-21K Pretraining for the Masses}, 
+  author={Tal Ridnik and Emanuel Ben-Baruch and Asaf Noy and Lihi Zelnik-Manor},
+  year={2021},
+  eprint={2104.10972},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```

config.json

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+{
+  "architecture": "tresnet_m",
+  "num_classes": 1000,
+  "num_features": 2048,
+  "pretrained_cfg": {
+    "tag": "miil_in21k_ft_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bilinear",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "body.conv1.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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pytorch_model.bin

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