pcvlab/unetplusplus_normal_vs_pvd_selective_pooling_30

UNETPLUSPLUS — Normal Vs Pvd Selective Pooling 30

Trained model weights for PVD classification with top-30% frame selection (normal vs. PVD) using ocular ultrasound videos.

Resource	Link
Paper
Dataset
Checkpoints
Code

Model Details

Property	Value
Architecture	UNet++ (features=(32,32,64,128,256,32))
Input modality	3D ocular ultrasound video
Input shape	[1, 96, 128, 128] (C, D, H, W)
Pooling	Top-K (k=30% of frames)
Output	Binary classification (sigmoid)

Labels

Label	Class
0	Normal
1	Posterior Vitreous Detachment

Usage

pip install git+https://github.com/OSUPCVLab/ERDES.git ultralytics

import torch
import numpy as np
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from ultralytics import YOLO
from erdes.models.components.cls_model import UNetPlusPlusClassifier
from erdes.data.components.utils import resize

# --- 1. Load YOLO for ocular globe detection ---
yolo = YOLO(hf_hub_download("pcvlab/yolov8_ocular_ultrasound_globe_detection", "yolov8_ocular_ultrasound_globe_detection.pt"))

# --- 2. Crop your POCUS ultrasound video using YOLO (finds largest globe bbox across all frames) ---
def crop_video(video_path, model, conf=0.8):
    # First pass: find the largest bounding box across all frames
    area_max, cropping_bbox = 0, None
    for frame in model.predict(video_path, stream=True, verbose=False, conf=conf):
        if len(frame.boxes.xywhn):
            bbox = frame.boxes.xywhn[0].cpu().numpy()
            area = bbox[2] * bbox[3]
            if area > area_max:
                area_max, cropping_bbox = area, bbox

    if cropping_bbox is None:
        raise ValueError("YOLO could not detect ocular globe in video.")

    # Second pass: crop every frame with the largest bbox
    frames = []
    for frame in model.predict(video_path, stream=True, verbose=False, conf=conf):
        img = frame.orig_img                                    # [H, W, C] BGR
        h, w, _ = img.shape
        x_c, y_c, bw, bh = cropping_bbox
        x1, y1 = int((x_c - bw/2) * w), int((y_c - bh/2) * h)
        x2, y2 = int((x_c + bw/2) * w), int((y_c + bh/2) * h)
        frames.append(img[y1:y2, x1:x2])

    return np.stack(frames)                                     # [D, H, W, C]

frames = crop_video("your_video.mp4", yolo)                    # [D, H, W, C]

# --- 3. Preprocess ---
video = torch.from_numpy(frames).float()                       # [D, H, W, C]
video = video.permute(3, 0, 1, 2)                              # [C, D, H, W]
if video.shape[0] == 3:
    video = video.mean(dim=0, keepdim=True)                    # grayscale [1, D, H, W]
video = resize((96, 128, 128))(video) / 255.0                  # pad + resize + normalize
video = video.unsqueeze(0)                                      # [1, 1, 96, 128, 128]

# --- 4. Load model and run inference ---
model = UNetPlusPlusClassifier(in_channels=1, num_classes=1, pooling="topk", topk_ratio=0.3)
weights = load_file(hf_hub_download("pcvlab/unetplusplus_normal_vs_pvd_selective_pooling_30", "model.safetensors"))
model.load_state_dict(weights)
model.eval()

with torch.no_grad():
    logit = model(video)
    prob = torch.sigmoid(logit).item()
    pred = int(prob > 0.5)

labels = {'0': 'Normal', '1': 'Posterior Vitreous Detachment'}
print(f"Prediction: {labels[str(pred)]} (confidence: {prob:.3f})")

Citation

If you use this model, please cite the ERDES paper:

@misc{ozkut2026erdes,
  title={ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound},
  author={Yasemin Ozkut and Pouyan Navard and Srikar Adhikari and Elaine Situ-LaCasse and Josie Acu{\~n}a and Adrienne Yarnish and Alper Yilmaz},
  year={2026},
  eprint={2508.04735},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2508.04735}
}