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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 nextvit_base.bd_in1k_384 |
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A Next-ViT image classification model. Trained on ImageNet-1k by paper authors. |
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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): 44.8 |
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- GMACs: 24.2 |
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- Activations (M): 66.0 |
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- Image size: 384 x 384 |
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- **Dataset:** ImageNet-1k |
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- **Papers:** |
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- Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios: https://arxiv.org/abs/2207.05501 |
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- **Original:** https://github.com/bytedance/Next-ViT |
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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('nextvit_base.bd_in1k_384', 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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'nextvit_base.bd_in1k_384', |
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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, 96, 96, 96]) |
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# torch.Size([1, 256, 48, 48]) |
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# torch.Size([1, 512, 24, 24]) |
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# torch.Size([1, 1024, 12, 12]) |
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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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'nextvit_base.bd_in1k_384', |
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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, 1024, 12, 12) 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| |
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|---------------------------------|------|--------|------|--------|-----------| |
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|nextvit_large.bd_ssld_6m_in1k_384|86.542|13.458 |98.142|1.858 |57.87 | |
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|nextvit_base.bd_ssld_6m_in1k_384 |86.352|13.648 |98.04 |1.96 |44.82 | |
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|nextvit_small.bd_ssld_6m_in1k_384|85.964|14.036 |97.908|2.092 |31.76 | |
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|nextvit_large.bd_ssld_6m_in1k |85.48 |14.52 |97.696|2.304 |57.87 | |
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|nextvit_base.bd_ssld_6m_in1k |85.186|14.814 |97.59 |2.41 |44.82 | |
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|nextvit_large.bd_in1k_384 |84.924|15.076 |97.294|2.706 |57.87 | |
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|nextvit_small.bd_ssld_6m_in1k |84.862|15.138 |97.382|2.618 |31.76 | |
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|nextvit_base.bd_in1k_384 |84.706|15.294 |97.224|2.776 |44.82 | |
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|nextvit_small.bd_in1k_384 |84.022|15.978 |96.99 |3.01 |31.76 | |
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|nextvit_large.bd_in1k |83.626|16.374 |96.694|3.306 |57.87 | |
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|nextvit_base.bd_in1k |83.472|16.528 |96.656|3.344 |44.82 | |
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|nextvit_small.bd_in1k |82.61 |17.39 |96.226|3.774 |31.76 | |
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## Citation |
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```bibtex |
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@article{li2022next, |
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title={Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios}, |
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author={Li, Jiashi and Xia, Xin and Li, Wei and Li, Huixia and Wang, Xing and Xiao, Xuefeng and Wang, Rui and Zheng, Min and Pan, Xin}, |
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journal={arXiv preprint arXiv:2207.05501}, |
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year={2022} |
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} |
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``` |
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