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---
tags:
- timm
- feature-extraction
- image-classification
library_name: timm
license: other
license_name: kaiko-non-commercial
license_link: https://github.com/kaiko-ai/towards_large_pathology_fms/blob/a62a0c54719d858371aefa0fcab6ec4b34c86c4c/LICENSE
metrics:
- accuracy
model-index:
- name: kaiko
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: BACH
type: image-classification
metrics:
- type: accuracy
value: 0.797
name: Accuracy
verified: false
- task:
type: image-classification
name: Image Classification
dataset:
name: CRC-NCT-HE
type: image-classification
metrics:
- type: accuracy
value: 0.943
name: Accuracy
verified: false
- task:
type: image-classification
name: Image Classification
dataset:
name: MHIST
type: image-classification
metrics:
- type: accuracy
value: 0.828
name: Accuracy
verified: false
- task:
type: image-classification
name: Image Classification
dataset:
name: PCam
type: image-classification
metrics:
- type: accuracy
value: 0.893
name: Accuracy
verified: false
- task:
type: image-classification
name: Image Classification
dataset:
name: TP53
type: image-classification
metrics:
- type: accuracy
value: 0.633
name: Accuracy
verified: false
- task:
type: image-classification
name: Image Classification
dataset:
name: CoNSeP
type: image-classification
metrics:
- type: accuracy
value: 0.649
name: Accuracy
verified: false
---
# Model card for vit_small_patch16_224.kaiko_ai_towards_large_pathology_fms
## Model Details
- **Model Type:** Feature backbone
- **Model Stats:**
- Params: 22M (small)
- Image size: 224 x 224 x 3
- Patch size: 16 x 16 x 3
- **Repository:** [github.com:kaiko-ai/towards_large_pathology_fms](https://github.com/kaiko-ai/towards_large_pathology_fms)
- **Original Weights:** [github.com:kaiko-ai/towards_large_pathology_fms/0.0.1](https://github.com/kaiko-ai/towards_large_pathology_fms/releases/tag/0.0.1)
- **Papers:**
- [Towards Large-Scale Training of Pathology Foundation Models](https://arxiv.org/abs/2404.15217)
## Model Usage
### Image Embeddings
```python
from torchvision.transforms import v2
from PIL import Image
import requests
import torch
import timm
import io
# get example histology image
url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQc7_xZpGOfQT7sxKwf2w5lL4GAq6IX_CbTzP1NGeenzA&s"
image = Image.open(io.BytesIO(requests.get(url).content))
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/vit_small_patch16_224.kaiko_ai_towards_large_pathology_fms",
dynamic_img_size=True,
pretrained=True,
).eval()
# get image transform
preprocessing = v2.Compose(
[
v2.ToImage(),
v2.Resize(size=224),
v2.CenterCrop(size=224),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
),
]
)
data = preprocessing(image).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{ai2024largescale,
title = {Towards Large-Scale Training of Pathology Foundation Models},
author = {kaiko.ai and Nanne Aben and Edwin D. de Jong and Ioannis Gatopoulos and Nicolas Känzig and Mikhail Karasikov and Axel Lagré and Roman Moser and Joost van Doorn and Fei Tang},
year = {2024},
eprint = {2404.15217},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
```
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