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--- |
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tags: |
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- image-classification |
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- ecology |
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- fungi |
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- FGVC |
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library_name: DanishFungi |
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license: cc-by-nc-4.0 |
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--- |
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# Model card for BVRA/legacy_seresnext101_32x4d.in1k_ft_df20_299 |
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## Model Details |
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- **Model Type:** Danish Fungi Classification |
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- **Model Stats:** |
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- Params (M): 50.2M |
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- Image size: 299 x 299 |
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- **Papers:** |
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- **Original 1:** Aggregated Residual Transformations for Deep Neural Networks --> https://arxiv.org/pdf/1611.05431v2 |
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- **Original 2:** Squeeze-and-Excitation Networks --> https://arxiv.org/pdf/1709.01507v4 |
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- **Train Dataset:** DF20 --> https://github.com/BohemianVRA/DanishFungiDataset/ |
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## Model Usage |
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### Image Embeddings |
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```python |
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import timm |
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import torch |
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import torchvision.transforms as T |
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from PIL import Image |
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from urllib.request import urlopen |
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model = timm.create_model("hf-hub:BVRA/legacy_seresnext101_32x4d.in1k_ft_df20_299", pretrained=True) |
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model = model.eval() |
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train_transforms = T.Compose([T.Resize((299, 299)), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) |
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img = Image.open(PATH_TO_YOUR_IMAGE) |
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output = model(train_transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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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{Picek_2022_WACV, |
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author = {Picek, Lukas and Sulc, Milan and Matas, Jiri and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, |
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title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, |
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booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, |
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month = {January}, |
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year = {2022}, |
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pages = {1525-1535} |
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} |
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@article{picek2022automatic, |
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title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, |
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author={Picek, Lukas and Sulc, Milan and Matas, Jiri and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, |
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journal={Sensors}, |
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volume={22}, |
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number={2}, |
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pages={633}, |
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year={2022}, |
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publisher={Multidisciplinary Digital Publishing Institute} |
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} |
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``` |