Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: other
+datasets:
+- imagenet-1k
+---
+# Model card for mobilevitv2_125.cvnets_in1k
+
+A MobileViT-v2 image classification model. Trained on ImageNet-1k by paper authors.
+
+See license details at https://github.com/apple/ml-cvnets/blob/main/LICENSE
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 7.5
+  - GMACs: 2.9
+  - Activations (M): 20.1
+  - Image size: 256 x 256
+- **Papers:**
+  - Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680
+- **Original:** https://github.com/apple/ml-cvnets
+- **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('mobilevitv2_125.cvnets_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(
+    'mobilevitv2_125.cvnets_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, 80, 128, 128])
+    #  torch.Size([1, 160, 64, 64])
+    #  torch.Size([1, 320, 32, 32])
+    #  torch.Size([1, 480, 16, 16])
+    #  torch.Size([1, 640, 8, 8])
+
+    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(
+    'mobilevitv2_125.cvnets_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, 640, 8, 8) 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).
+
+## Citation
+```bibtex
+@article{Mehta2022SeparableSF,
+  title={Separable Self-attention for Mobile Vision Transformers},
+  author={Sachin Mehta and Mohammad Rastegari},
+  journal={ArXiv},
+  year={2022},
+  volume={abs/2206.02680}
+}
+```

config.json

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+{
+  "architecture": "mobilevitv2_125",
+  "num_classes": 1000,
+  "num_features": 640,
+  "pretrained_cfg": {
+    "tag": "cvnets_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      256,
+      256
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 0.888,
+    "crop_mode": "center",
+    "mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      8,
+      8
+    ],
+    "first_conv": "stem.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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pytorch_model.bin

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