Model card for BVRA/legacy_seresnext101_32x4d.in1k_ft_df20m_299
Model Details
- Model Type: Danish Fungi Classification
- Model Stats:
- Params (M): 47.3M
- Image size: 299 x 299
- Papers:
- Original 1: Aggregated Residual Transformations for Deep Neural Networks --> https://arxiv.org/pdf/1611.05431v2
- Original 2: Squeeze-and-Excitation Networks --> https://arxiv.org/pdf/1709.01507v4
- Train Dataset: DF20 --> https://github.com/BohemianVRA/DanishFungiDataset/
Model Usage
Image Embeddings
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/legacy_seresnext101_32x4d.in1k_ft_df20m_299", pretrained=True)
model = model.eval()
train_transforms = T.Compose([T.Resize((299, 299)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
img = Image.open(PATH_TO_YOUR_IMAGE)
output = model(train_transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
Citation
@InProceedings{Picek_2022_WACV,
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},
title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2022},
pages = {1525-1535}
}
@article{picek2022automatic,
title={Automatic Fungi Recognition: Deep Learning Meets Mycology},
author={Picek, Lukas and Sulc, Milan and Matas, Jiri and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil},
journal={Sensors},
volume={22},
number={2},
pages={633},
year={2022},
publisher={Multidisciplinary Digital Publishing Institute}
}
- Downloads last month
- 2
Inference API (serverless) does not yet support DanishFungi models for this pipeline type.