GradeEye four-ch-soft

This repository contains GradeEye CORN ordinal diabetic-retinopathy classifiers using the convnext_tiny backbone at 384x384 resolution, with 4-channel input and a 4-threshold ordinal head. The _ema.safetensors file is the primary artifact: the paper's reported evaluation metrics were generated using the EMA state dict. The unsuffixed .safetensors file is the corresponding raw model_state_dict secondary artifact.

Checkpoints

Primary EMA weights	Raw secondary weights	Architecture	Held-out fold	Best QWK	Epoch
lodo_aptos_convnext_tiny_best_ema.safetensors	lodo_aptos_convnext_tiny_best.safetensors	convnext_tiny	varies	0.7716	5
lodo_ddr_convnext_tiny_best_ema.safetensors	lodo_ddr_convnext_tiny_best.safetensors	convnext_tiny	varies	0.7775	7
lodo_eyepacs_convnext_tiny_best_ema.safetensors	lodo_eyepacs_convnext_tiny_best.safetensors	convnext_tiny	varies	0.7950	9
lodo_messidor2_convnext_tiny_best_ema.safetensors	lodo_messidor2_convnext_tiny_best.safetensors	convnext_tiny	varies	0.7415	5

Preprocessing

1. Resize the RGB fundus image to 384x384 using the same offline preprocessing pipeline.
2. Convert RGB to float in [0,1] and apply ImageNet normalization: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225).
3. Append one auxiliary channel in [0,1] after RGB normalization. It is not ImageNet-normalized.

Fourth-channel construction

• Source pool: segmentation_pooled_soft.
• Producer: gradeeye/seg-unet-bcedice.
• Exact method: Raw sigmoid probability map from the BCE+Dice-trained U-Net; uint16 PNG divided by 65535 to [0,1]. No morphological post-processing.
• The resulting tensor is (4, 384, 384) in channel-first layout.
• No opening, closing, or Gaussian blur is applied in this variant.

Loading

import json
from modeling import load_model

config = json.load(open('config.json'))
model = load_model('lodo_eyepacs_convnext_tiny_best_ema.safetensors', config)
# model(x) returns CORN logits with shape (batch, 4)

Install torch, timm, and safetensors, and make the GradeEye source repository available on PYTHONPATH.

Intended use and limitations

These weights are released for research and reproducibility only. They are not validated for clinical diagnosis or treatment decisions. Performance varies substantially by held-out dataset and should not be interpreted as clinical-grade generalization.

Source code and paper materials: https://github.com/ahmed-farhanur-rashid/gradeeye.