Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_tag: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+- imagenet-12k
+---
+# Model card for rexnetr_200.sw_in12k_ft_in1k
+
+A ReXNet-R image classification model. The R variant of the architecture is `timm` specific and rounds channels (modulus 8 or 16) to prevent performance issues w/ NVIDIA Tensor Cores. Pretrained on ImageNet-12k and fine-tuned on ImageNet-1k by Ross Wightman in `timm`.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 16.5
+  - GMACs: 1.6
+  - Activations (M): 15.1
+  - Image size: train = 224 x 224, test = 288 x 288
+- **Papers:**
+  - Rethinking Channel Dimensions for Efficient Model Design: https://arxiv.org/abs/2007.00992
+- **Original:** https://github.com/huggingface/pytorch-image-models
+- **Dataset:** ImageNet-1k
+- **Pretrain Dataset:** ImageNet-12k
+
+## 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('rexnetr_200.sw_in12k_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(
+    'rexnetr_200.sw_in12k_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, 32, 112, 112])
+    #  torch.Size([1, 80, 56, 56])
+    #  torch.Size([1, 120, 28, 28])
+    #  torch.Size([1, 256, 14, 14])
+    #  torch.Size([1, 368, 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(
+    'rexnetr_200.sw_in12k_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, 2560, 7, 7) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results)."
+
+|model                    |top1  |top5  |param_count|img_size|crop_pct|
+|-------------------------|------|------|-----------|--------|--------|
+|rexnetr_300.sw_in12k_ft_in1k|84.53 |97.252|34.81      |288     |1.0     |
+|rexnetr_200.sw_in12k_ft_in1k|83.164|96.648|16.52      |288     |1.0     |
+|rexnet_300.nav_in1k      |82.772|96.232|34.71      |224     |0.875   |
+|rexnet_200.nav_in1k      |81.652|95.668|16.37      |224     |0.875   |
+|rexnet_150.nav_in1k      |80.308|95.174|9.73       |224     |0.875   |
+|rexnet_130.nav_in1k      |79.478|94.68 |7.56       |224     |0.875   |
+|rexnet_100.nav_in1k      |77.832|93.886|4.8        |224     |0.875   |
+
+## Citation
+```bibtex
+@misc{han2021rethinking,
+  title={Rethinking Channel Dimensions for Efficient Model Design}, 
+  author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
+  year={2021},
+  eprint={2007.00992},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}  
+```
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
+}
+```

config.json

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+{
+  "architecture": "rexnetr_200",
+  "num_classes": 1000,
+  "num_features": 2560,
+  "pretrained_cfg": {
+    "tag": "sw_in12k_ft_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "test_input_size": [
+      3,
+      288,
+      288
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "stem.conv",
+    "classifier": "head.fc",
+    "license": "apache-2.0"
+  }
+}

model.safetensors

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pytorch_model.bin

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