Add model
Browse files- README.md +90 -0
- config.json +36 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
README.md
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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_tag: 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 volo_d3_448.sail_in1k
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A VOLO (Vision Outlooker) image classification model. Trained on ImageNet-1k with token labelling 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): 86.6
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- GMACs: 96.3
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- Activations (M): 446.8
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- Image size: 448 x 448
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- **Papers:**
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- VOLO: Vision Outlooker for Visual Recognition: https://arxiv.org/abs/2106.13112
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- **Dataset:** ImageNet-1k
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- **Original:** https://github.com/sail-sg/volo
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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('volo_d3_448.sail_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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### 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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'volo_d3_448.sail_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, 785, 512) 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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## Citation
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```bibtex
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@article{yuan2022volo,
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title={Volo: Vision outlooker for visual recognition},
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author={Yuan, Li and Hou, Qibin and Jiang, Zihang and Feng, Jiashi and Yan, Shuicheng},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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year={2022},
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publisher={IEEE}
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}
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```
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config.json
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{
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"architecture": "volo_d3_448",
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"num_classes": 1000,
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"num_features": 512,
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"global_pool": "token",
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"pretrained_cfg": {
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"tag": "sail_in1k",
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"custom_load": false,
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"input_size": [
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3,
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448,
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448
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],
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"fixed_input_size": true,
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"interpolation": "bicubic",
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"crop_pct": 1.0,
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"crop_mode": "center",
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"mean": [
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0.485,
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0.456,
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0.406
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],
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"std": [
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0.229,
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0.224,
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0.225
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],
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"num_classes": 1000,
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"pool_size": null,
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"first_conv": "patch_embed.conv.0",
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"classifier": [
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"head",
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"aux_head"
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]
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b76c2422ead4c18afef8663b7612b6f1970e6da219665075ce15fbe0a1db175
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size 346550684
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:1864375f5c8848bd6d7d596d84583d0260c0ee5a7dd265a5bba5dab4da82159b
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size 346677461
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