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RamonK/
DistillPath-KS16-HOpt0

Name: DistillPath-KS16-HOpt0
Rating: 2.5 (24 reviews)

timm

safetensors

computational-pathology

knowledge-distillation

vision-transformer

medical-imaging

image-feature-extraction

TCGA

2608.17872

other

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DistillPath-KS16-HOpt0

A 22M ViT-S/16 pathology tile encoder distilled from H-optimus-0 (1.1B ViT-g/14) into the kaiko ViT-S/16 student using backbone-token distillation on 6,000 public TCGA slides.

Paper: DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance Ramon Kaspar, Andrey Ignatov, Valentina Boeva. ETH Zurich. Published at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MedFM-Bench).

Model details

Property	Value
Architecture	ViT-S/16 (`vit_small_patch16_224` in timm)
Parameters	21.7M
Feature dimension	384
Input size	224 x 224
Normalization	mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
Student initialization	kaiko ViT-S/16 (pathology-pretrained)
Teacher	H-optimus-0 (1.1B, ViT-g/14, Apache 2.0)
Training data	6,000 TCGA H&E whole-slide images, 32 cohorts
Training steps	50,000 (batch size 256, approx. 24-29 GPU-hours on 1x RTX 4090)

Benchmark results

Benchmark	DistillPath-KS16-HOpt0	kaiko baseline	H-optimus-0 teacher
EVA mean (7 tasks)	0.769	0.764	0.803
HEST mean (9 tasks)	0.376	0.349	0.415
PLISM score	0.480	0.307	0.480

See the paper for per-task results and comparisons with all four DistillPath variants.

Usage

Load directly from the Hub with timm:

python

import timm

model = timm.create_model(
    "hf_hub:RamonK/DistillPath-KS16-HOpt0",
    pretrained=True,
    num_classes=0,
)
model.eval()

Or load manually:

python

import timm
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

model = timm.create_model("vit_small_patch16_224", pretrained=False, num_classes=0)
path = hf_hub_download("RamonK/DistillPath-KS16-HOpt0", "model.safetensors")
state_dict = load_file(path)
model.load_state_dict(state_dict, strict=True)
model.eval()

This model uses normalization mean=(0.5, 0.5, 0.5) and std=(0.5, 0.5, 0.5), inherited from the kaiko student. Apply this normalization to input tiles before inference:

python

from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])

Distillation recipe

The recipe reads only the teacher's final class and patch tokens (no teacher pretraining heads required):

Class-token loss: cosine distance + RKD (relational knowledge distillation)
Patch-token loss: cosine distance after bicubic grid resizing (teacher 16x16 to student 14x14)
Optimizer: AdamW, lr=1e-4, weight decay 0.05, cosine decay, 500 warmup steps
Projector: DINO-style MLP (384 to 2048 to 2048 to 256 to d_t), discarded after training

Full details in the paper and the DistillPath repository.

License

This model is released under the Kaiko Non-Commercial Public License, inherited from the kaiko ViT-S/16 student weights. The teacher (H-optimus-0) is Apache 2.0.

Citation

bibtex

@inproceedings{kaspar2026distillpath,
  title     = {DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance},
  author    = {Kaspar, Ramon and Ignatov, Andrey and Boeva, Valentina},
  booktitle = {Medical Foundation Models and Benchmarks (MedFM-Bench), ECCV 2026},
  year      = {2026}
}