| | --- |
| | license: mit |
| | language: |
| | - en |
| | base_model: |
| | - sakshamkr1/ResNet50-APTOS-DR |
| | pipeline_tag: image-classification |
| | tags: |
| | - onnx |
| | --- |
| | # ResNet50-APTOS-DR (ONNX) |
| | **5-class Diabetic Retinopathy classifier** ready for edge devices. |
| |
|
| | **Original model**: sakshamkr1/ResNet50-APTOS-DR |
| | **Format**: ONNX |
| | **Input shape**: (batch, 3, 224, 224) RGB fundus image |
| | **Output**: 5 classes (APTOS 2019) |
| |
|
| | ### Classes |
| | - 0: No DR |
| | - 1: Mild DR |
| | - 2: Moderate DR |
| | - 3: Severe DR |
| | - 4: Proliferative DR |
| |
|
| | ### Perfect |
| | - Model size: ~105 MB (single file) |
| | - RAM usage: ~150-220 MB |
| | - Speed: ~0.8–1.5 seconds per image on CPU |
| |
|
| | ### Quick test code for colab |
| |
|
| | ```python |
| | # ============================ |
| | # 1. Install dependencies |
| | # ============================ |
| | !pip install -q onnxruntime huggingface_hub pillow torchvision matplotlib |
| | |
| | # ============================ |
| | # 2. Download the ONNX model |
| | # ============================ |
| | from huggingface_hub import hf_hub_download |
| | |
| | print("📥 Downloading iris-vit.onnx ...") |
| | model_path = hf_hub_download( |
| | repo_id="Shadow0482/iris-onnx", |
| | filename="iris-vit.onnx" |
| | ) |
| | print(f"✅ Model downloaded: {model_path}") |
| | |
| | # ============================ |
| | # 3. Load model & define inference |
| | # ============================ |
| | import onnxruntime as ort |
| | import numpy as np |
| | from PIL import Image |
| | import torchvision.transforms as transforms |
| | import matplotlib.pyplot as plt |
| | from google.colab import files |
| | |
| | # Load ONNX session (CPU is fine & fast for this ~105 MB model) |
| | session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"]) |
| | |
| | # Preprocessing (exactly what the model expects) |
| | transform = transforms.Compose([ |
| | transforms.Resize((224, 224)), |
| | transforms.ToTensor(), |
| | transforms.Normalize(mean=[0.485, 0.456, 0.406], |
| | std=[0.229, 0.224, 0.225]) |
| | ]) |
| | |
| | print("✅ Model loaded successfully!") |
| | |
| | # ============================ |
| | # 4. Upload a fundus image & run inference |
| | # ============================ |
| | print("\n📤 Please upload a color fundus/retina image (JPG/PNG)...") |
| | uploaded = files.upload() |
| | |
| | if uploaded: |
| | img_path = list(uploaded.keys())[0] |
| | img = Image.open(img_path).convert("RGB") |
| | |
| | # Preprocess |
| | input_tensor = transform(img).unsqueeze(0).numpy().astype(np.float32) |
| | |
| | # Inference |
| | outputs = session.run(None, {"input": input_tensor})[0][0] |
| | |
| | # Softmax |
| | exp_scores = np.exp(outputs) |
| | probs = exp_scores / np.sum(exp_scores) |
| | pred_idx = np.argmax(probs) |
| | |
| | classes = ["No DR", "Mild DR", "Moderate DR", "Severe DR", "Proliferative DR"] |
| | |
| | print(f"\n🎯 **Prediction:** {classes[pred_idx]}") |
| | print(f" Confidence: {probs[pred_idx]*100:.1f}%") |
| | print("\n📊 Full probabilities:") |
| | for name, p in zip(classes, probs): |
| | print(f" {name:20} → {p*100:5.1f}%") |
| | |
| | # Show image |
| | plt.figure(figsize=(8, 6)) |
| | plt.imshow(img) |
| | plt.title(f"Predicted: {classes[pred_idx]} ({probs[pred_idx]*100:.1f}%)", fontsize=14) |
| | plt.axis("off") |
| | plt.show() |
| | ``` |
| |
|
| | **License**: MIT |
| | Made for low-resource diabetic retinopathy screening ❤️ |
