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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/inception_v4.ft_in1k_df20m_299 |
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## Model Details |
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- **Model Type:** inception_v4.ft_in1k_df20m_299 |
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- **Model Stats:** |
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- Params (M): ?? |
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- Image size: 299 x 299 |
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- **Papers:** |
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- **Original:** ?? |
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- **Train Dataset:** DF20M --> https://sites.google.com/view/danish-fungi-dataset |
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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/inception_v4.ft_in1k_df20m_299", pretrained=True) |
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model = model.eval() |
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train_transforms = T.Compose([T.Resize(299), |
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T.ToTensor(), |
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T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) |
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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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https://openaccess.thecvf.com/content/WACV2022/papers/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.pdf |
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