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license: cc-by-nc-4.0
library_name: transformers
tags:
- pathology
- vision
- vit
- feature-extraction
- knowledge-distillation
- pytorch
pipeline_tag: image-feature-extraction
---
# PathAGG
Multi-teacher pathology foundation students distilled with **CRADIOv4**-style aggregation from [Virchow2](https://huggingface.co/paige-ai/Virchow2), [UNI2-h](https://huggingface.co/MahmoodLab/UNI2-h), and [H1](https://huggingface.co/bioptimus/H-optimus-1) (H-optimus-1).
Trained on **~66M** TCGA + HISTAI patches (~**1000 GPU-hours** / student on 8× H100).
Code & EVA dumps: [Luoxd1996/PathAGG](https://github.com/Luoxd1996/PathAGG).
| Variant | `subfolder` | Embed | Official EVA avg (9 tasks) |
|---------|-------------|------:|---------------------------:|
| ViT-B/14 | `vitb` | 768 | **79.57** |
| ViT-S/14 | `vits` | 384 | **77.93** |
**License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — non-commercial / research only.**
## Load (recommended)
```bash
pip install torch timm transformers torchvision Pillow
```
```python
import torch
from PIL import Image
from torchvision import transforms
from transformers import AutoModel
preprocess = transforms.Compose([
transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
model = AutoModel.from_pretrained(
"luoxd96/PathAGG",
subfolder="vitb", # or "vits"
trust_remote_code=True,
).eval().cuda()
img = Image.open("patch.png").convert("RGB")
x = preprocess(img).unsqueeze(0).cuda()
with torch.inference_mode():
cls = model(x) # [1, 768] or [1, 384]
cls, patch = model(x, return_patch=True) # patch: [1, 256, D]
```
## EVA results (%)
| Task | Split | ViT-S | ViT-B |
|------|-------|------:|------:|
| BreakHis | val | 74.36 | 84.62 |
| CRC | val | 95.93 | 96.41 |
| Gleason | val | 78.72 | 77.33 |
| MHIST | val | 81.85 | 82.20 |
| PCam | test | 93.88 | 93.75 |
| Cam16Small | test | 83.59 | 85.03 |
| PANDASmall | test | 66.28 | 67.93 |
| CoNSeP | val | 63.58 | 64.04 |
| MoNuSAC | val | 63.22 | 64.82 |
| **Official Avg** | | **77.93** | **79.57** |
|