Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_tag: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for levit_conv_128s.fb_dist_in1k
+
+A LeViT image classification model using default linear mode (non-convolutional mode with nn.Linear and nn.BatchNorm1d). Pretrained on ImageNet-1k using distillation by paper authors.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 7.8
+  - GMACs: 0.3
+  - Activations (M): 1.9
+  - Image size: 224 x 224
+- **Papers:**
+  - LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference: https://arxiv.org/abs/2104.01136
+- **Original:** https://github.com/facebookresearch/LeViT
+- **Dataset:** ImageNet-1k
+
+## 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('levit_conv_128s.fb_dist_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)
+```
+
+### 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(
+    'levit_conv_128s.fb_dist_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 (ie.e a (batch_size, num_features, H, W) tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is (batch_size, num_features) tensor
+```
+
+### 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(
+    'levit_conv_128s.fb_dist_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. for levit_conv_256: 
+    # torch.Size([2, 256, 14, 14])
+    # torch.Size([2, 384, 7, 7])
+    # torch.Size([2, 512, 4, 4])
+    print(o.shape)
+```
+
+## Model Comparison
+|model                              |top1  |top5  |param_count|img_size|
+|-----------------------------------|------|------|-----------|--------|
+|levit_384.fb_dist_in1k             |82.596|96.012|39.13      |224     |
+|levit_conv_384.fb_dist_in1k        |82.596|96.012|39.13      |224     |
+|levit_256.fb_dist_in1k             |81.512|95.48 |18.89      |224     |
+|levit_conv_256.fb_dist_in1k        |81.512|95.48 |18.89      |224     |
+|levit_conv_192.fb_dist_in1k        |79.86 |94.792|10.95      |224     |
+|levit_192.fb_dist_in1k             |79.858|94.792|10.95      |224     |
+|levit_128.fb_dist_in1k             |78.474|94.014|9.21       |224     |
+|levit_conv_128.fb_dist_in1k        |78.474|94.02 |9.21       |224     |
+|levit_128s.fb_dist_in1k            |76.534|92.864|7.78       |224     |
+|levit_conv_128s.fb_dist_in1k       |76.532|92.864|7.78       |224     |
+
+## Citation
+```bibtex
+@InProceedings{Graham_2021_ICCV,
+  author    = {Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and Jegou, Herve and Douze, Matthijs},
+  title     = {LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  month     = {October},
+  year      = {2021},
+  pages     = {12259-12269}
+}
+```
+```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/rwightman/pytorch-image-models}}
+}
+```

config.json

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+{
+  "architecture": "levit_conv_128s",
+  "num_classes": 1000,
+  "num_features": 384,
+  "global_pool": "avg",
+  "pretrained_cfg": {
+    "tag": "fb_dist_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": true,
+    "interpolation": "bicubic",
+    "crop_pct": 0.9,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      4,
+      4
+    ],
+    "first_conv": "stem.conv1.linear",
+    "classifier": [
+      "head.linear",
+      "head_dist.linear"
+    ]
+  }
+}

pytorch_model.bin

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