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---
library_name: peft
base_model: google/vit-large-patch16-224
---
## LoRA Image Binary Classification LoRA adapter
Trained on APTOS 2019 Kaggle competition for identifying diabetic retinopathy. In this case I've modified the problem
to binary classifier (diagnosis=0 vs. all others; 50-50% distribution in training data)
Base Model: [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224)
Dataset: https://www.kaggle.com/c/aptos2019-blindness-detection - fundus images of the back of the eye, and diabetic retinopathy score
Training notebook: https://colab.research.google.com/drive/1TVsUyyou87E26Sz40CdBH3CzWoVckgtq?usp=sharing
On 10% held-out of training data: accuracy 98%
- PEFT 0.5.0
PEFT Image classifier inference / [Gradio app](https://huggingface.co/spaces/monsoon-nlp/eyegazer-demo/blob/main/app.py)
```python
from peft import PeftModel
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
model_name = 'google/vit-large-patch16-224'
adapter = 'monsoon-nlp/eyegazer-vit-binary'
image_processor = AutoImageProcessor.from_pretrained(model_name)
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
train_transforms = Compose(
[
RandomResizedCrop(image_processor.size["height"]),
RandomHorizontalFlip(),
ToTensor(),
normalize,
]
)
val_transforms = Compose(
[
Resize(image_processor.size["height"]),
CenterCrop(image_processor.size["height"]),
ToTensor(),
normalize,
]
)
model = AutoModelForImageClassification.from_pretrained(
model_name,
ignore_mismatched_sizes=True,
num_labels=2,
)
lora_model = PeftModel.from_pretrained(model, adapter)
img = Image.open("sample.png")
pimg = val_transforms(img.convert("RGB"))
batch = pimg.unsqueeze(0)
op = lora_model(batch)
vals = op.logits.tolist()[0]
if vals[0] > vals[1]:
return "Predicted unaffected"
else:
return "Predicted affected to some degree"
```
## Future goals
- More documentation
- Modify loss for regression on 0-4 score |