| tags: | |
| - image-classification | |
| - ecology | |
| - fungi | |
| - FGVC | |
| library_name: DanishFungi | |
| license: cc-by-nc-4.0 | |
| # Model card for BVRA/DF23M-inception_v4.tf_in1k-299 | |
| ## Model Details | |
| - **Model Type:** inception_v4.tf_in1k | |
| - **Model Stats:** | |
| - Params (M): ?? | |
| - Image size: 299 x 299 | |
| - **Papers:** | |
| - **Original:** ?? | |
| - **Train Dataset:** DF23M --> https://sites.google.com/view/danish-fungi-dataset | |
| ## Model Usage | |
| ### Image Embeddings | |
| ```python | |
| import timm | |
| import torch | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| from urllib.request import urlopen | |
| model = timm.create_model("hf-hub:BVRA/DF23M-inception_v4.tf_in1k-299", pretrained=True) | |
| model = model.eval() | |
| train_transforms = T.Compose([T.Resize(299), | |
| T.ToTensor(), | |
| T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) | |
| img = Image.open(PATH_TO_YOUR_IMAGE) | |
| output = model(train_transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor | |
| # output is a (1, num_features) shaped tensor | |
| ``` | |
| ## Citation | |
| https://openaccess.thecvf.com/content/WACV2022/papers/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.pdf | |