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--- |
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tags: |
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- image-classification |
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- timm |
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library_name: timm |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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--- |
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# Model card for mobilenetv4_hybrid_medium.e500_r224_in1k |
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A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman. |
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Trained with `timm` scripts using hyper-parameters (mostly) similar to those in the paper. |
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NOTE: So far, these are the only known MNV4 weights. Official weights for Tensorflow models are unreleased. |
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## Model Details |
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Params (M): 11.1 |
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- GMACs: 1.0 |
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- Activations (M): 6.4 |
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- Image size: train = 224 x 224, test = 256 x 256 |
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- **Dataset:** ImageNet-1k |
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- **Papers:** |
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- MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518 |
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- **Original:** https://github.com/tensorflow/models/tree/master/official/vision |
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## Model Usage |
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### Image Classification |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model('mobilenetv4_hybrid_medium.e500_r224_in1k', pretrained=True) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) |
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``` |
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### Feature Map Extraction |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'mobilenetv4_hybrid_medium.e500_r224_in1k', |
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pretrained=True, |
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features_only=True, |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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for o in output: |
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# print shape of each feature map in output |
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# e.g.: |
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# torch.Size([1, 32, 112, 112]) |
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# torch.Size([1, 48, 56, 56]) |
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# torch.Size([1, 80, 28, 28]) |
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# torch.Size([1, 160, 14, 14]) |
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# torch.Size([1, 960, 7, 7]) |
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print(o.shape) |
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``` |
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### Image Embeddings |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'mobilenetv4_hybrid_medium.e500_r224_in1k', |
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pretrained=True, |
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num_classes=0, # remove classifier nn.Linear |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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# or equivalently (without needing to set num_classes=0) |
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output = model.forward_features(transforms(img).unsqueeze(0)) |
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# output is unpooled, a (1, 960, 7, 7) shaped tensor |
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output = model.forward_head(output, pre_logits=True) |
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# output is a (1, num_features) shaped tensor |
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``` |
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## Model Comparison |
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### By Top-1 |
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|model |top1 |top1_err|top5 |top5_err|param_count|img_size| |
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|-------------------------------------------|------|--------|------|--------|-----------|--------| |
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|mobilenetv4_conv_large.e500_r256_in1k |82.674|17.326 |96.31 |3.69 |32.59 |320 | |
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|mobilenetv4_conv_large.e500_r256_in1k |81.862|18.138 |95.69 |4.31 |32.59 |256 | |
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|mobilenetv4_hybrid_medium.e500_r224_in1k |81.276|18.724 |95.742|4.258 |11.07 |256 | |
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|mobilenetv4_conv_medium.e500_r256_in1k |80.858|19.142 |95.768|4.232 |9.72 |320 | |
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|mobilenetv4_hybrid_medium.e500_r224_in1k |80.442|19.558 |95.38 |4.62 |11.07 |224 | |
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|mobilenetv4_conv_blur_medium.e500_r224_in1k|80.142|19.858 |95.298|4.702 |9.72 |256 | |
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|mobilenetv4_conv_medium.e500_r256_in1k |79.928|20.072 |95.184|4.816 |9.72 |256 | |
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|mobilenetv4_conv_medium.e500_r224_in1k |79.808|20.192 |95.186|4.814 |9.72 |256 | |
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|mobilenetv4_conv_blur_medium.e500_r224_in1k|79.438|20.562 |94.932|5.068 |9.72 |224 | |
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|mobilenetv4_conv_medium.e500_r224_in1k |79.094|20.906 |94.77 |5.23 |9.72 |224 | |
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|mobilenetv4_conv_small.e1200_r224_in1k |74.292|25.708 |92.116|7.884 |3.77 |256 | |
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|mobilenetv4_conv_small.e1200_r224_in1k |73.454|26.546 |91.34 |8.66 |3.77 |224 | |
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