Add model
Browse files- README.md +136 -0
- config.json +42 -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 edgenext_small.usi_in1k
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An EdgeNeXt image classification model. Trained on ImageNet-1k by paper authors using distillation (`USI` as per `Solving ImageNet`).
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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): 5.6
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- GMACs: 1.3
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- Activations (M): 9.1
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- Image size: train = 256 x 256, test = 320 x 320
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- **Papers:**
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- EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications: https://arxiv.org/abs/2206.10589
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- Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results: https://arxiv.org/abs/2204.03475
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- **Dataset:** ImageNet-1k
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- **Original:** https://github.com/mmaaz60/EdgeNeXt
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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('edgenext_small.usi_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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'edgenext_small.usi_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, 48, 64, 64])
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# torch.Size([1, 96, 32, 32])
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# torch.Size([1, 160, 16, 16])
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# torch.Size([1, 304, 8, 8])
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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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'edgenext_small.usi_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, 304, 8, 8) 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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@inproceedings{Maaz2022EdgeNeXt,
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title={EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications},
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author={Muhammad Maaz and Abdelrahman Shaker and Hisham Cholakkal and Salman Khan and Syed Waqas Zamir and Rao Muhammad Anwer and Fahad Shahbaz Khan},
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booktitle={International Workshop on Computational Aspects of Deep Learning at 17th European Conference on Computer Vision (CADL2022)},
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year={2022},
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organization={Springer}
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}
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```
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```bibtex
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@misc{https://doi.org/10.48550/arxiv.2204.03475,
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doi = {10.48550/ARXIV.2204.03475},
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url = {https://arxiv.org/abs/2204.03475},
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author = {Ridnik, Tal and Lawen, Hussam and Ben-Baruch, Emanuel and Noy, Asaf},
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keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results},
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publisher = {arXiv},
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year = {2022},
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}
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```
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config.json
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{
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"architecture": "edgenext_small",
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"num_classes": 1000,
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"num_features": 304,
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"global_pool": "avg",
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"pretrained_cfg": {
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"tag": "usi_in1k",
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"custom_load": false,
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"input_size": [
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3,
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256,
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256
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],
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"test_input_size": [
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3,
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320,
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320
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],
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"fixed_input_size": false,
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"interpolation": "bicubic",
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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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8,
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8
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],
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"first_conv": "stem.0",
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"classifier": "head.fc"
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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 sha256:14d824931ca7f09e631c90347253bbc7a925ad017a02d7b8bf6e13b872d8059b
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size 22368078
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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:d5f9c753e70494b6534c2164dead151b2ea8cec185ce787898f6adef5457b8b4
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size 22425061
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