File size: 2,042 Bytes
09959a5 d49cfee 09959a5 d49cfee 09959a5 d49cfee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
tags:
- timm
- feature-extraction
- image-classification
library_name: timm
license: apache-2.0
---
# Model card for vit_giant_patch14_224.dinobloom
## Model Details
- **Model Type:** Feature backbone
- **Model Stats:**
- Params: 1136M (giant)
- Image size: 224 x 224 x 3
- Patch size: 14 x 14 x 3
- **Repository:** [github.com:marrlab/DinoBloom](https://github.com/marrlab/DinoBloom)
- **Original Weights:** [Zenodo](https://zenodo.org/records/10908163)
- **License:** [Apache License 2.0](https://github.com/marrlab/DinoBloom/blob/main/LICENSE)
- **Papers:**
- [DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology](https://arxiv.org/abs/2404.05022)
## Model Usage
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://raw.githubusercontent.com/zxaoyou/segmentation_WBC/master/Dataset%201/001.bmp"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/vit_giant_patch14_224.dinobloom",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@misc{koch2024dinobloom,
title = {DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology},
author = {Valentin Koch and Sophia J. Wagner and Salome Kazeminia and Ece Sancar and Matthias Hehr and Julia Schnabel and Tingying Peng and Carsten Marr},
year = {2024},
eprint = {2404.05022},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
``` |