Add model
Browse files- README.md +94 -0
- config.json +41 -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_name: timm
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license: mit
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datasets:
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- imagenet-1k
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---
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# Model card for hrnet_w48_ssld.paddle_in1k
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A HRNet 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): 77.5
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- GMACs: 17.3
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- Activations (M): 28.6
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- Image size: train = 224 x 224, test = 288 x 288
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- **Papers:**
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- Deep High-Resolution Representation Learning for Visual Recognition: https://arxiv.org/abs/1908.07919
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- **Original:** https://github.com/HRNet/HRNet-Image-Classification
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- **Dataset:** ImageNet-1k
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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('hrnet_w48_ssld.paddle_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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'hrnet_w48_ssld.paddle_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, 2048, 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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Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
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## Citation
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```bibtex
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@article{WangSCJDZLMTWLX19,
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title={Deep High-Resolution Representation Learning for Visual Recognition},
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author={Jingdong Wang and Ke Sun and Tianheng Cheng and
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Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
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Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
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journal = {TPAMI}
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year={2019}
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}
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```
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config.json
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{
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"architecture": "hrnet_w48_ssld",
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"num_classes": 1000,
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"num_features": 2048,
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"pretrained_cfg": {
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"tag": "paddle_in1k",
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"custom_load": false,
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"input_size": [
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3,
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224,
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224
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],
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"test_input_size": [
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3,
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288,
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288
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],
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"fixed_input_size": false,
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"interpolation": "bilinear",
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"crop_pct": 0.95,
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"test_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": [
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7,
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7
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],
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"first_conv": "conv1",
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"classifier": "classifier"
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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:cdc85156d0dcc49fcf7a919a23374e105879c299389d57aa8dacd6a76e9a98ac
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size 310473508
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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:dab98e6b7e161ff27ae2b0f61d370db8bcb1e5f098db1d4e8a262d0535dd5411
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size 310958981
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