Create README.md

README.md

@@ -0,0 +1,132 @@
+---
+license: mit
+tags:
+  - semantic-segmentation
+  - unet
+  - resnet34
+  - steel-defect-detection
+  - severstal
+  - pytorch
+datasets:
+  - severstal-steel-defect-detection
+metrics:
+  - dice
+---
+
+# ResNet34‑UNet for Steel Defect Segmentation
+
+This repository hosts the **trained model weights** for a U‑Net with ResNet34 backbone, fine‑tuned for semantic segmentation of surface defects on steel sheets. The model classifies and localises four defect types and outputs pixel‑wise probability maps.
+
+> 🔗 **Full training and inference code is available on GitHub:**  
+> [https://github.com/zyxdtt/cv-course-project/tree/main/Semantic%20Segmentation](https://github.com/zyxdtt/cv-course-project/tree/main/Semantic%20Segmentation)
+
+---
+
+## 🧠 Model Description
+
+| Property | Details |
+|----------|---------|
+| Architecture | U‑Net with encoder: ResNet34 (pretrained on ImageNet) |
+| Input size | 256 × 1600 pixels (single channel grayscale converted to RGB for pretrained backbone) |
+| Output | 4 probability maps (height 256, width 1600) with sigmoid activation |
+| Loss function | `L = BinaryCrossEntropy + (1 - Dice)` |
+| Optimiser | AdamW (initial LR = 1e-4) |
+| Training epochs | 10 |
+| Data augmentation | Random horizontal flip (p=0.5) |
+
+---
+
+## 📊 Performance on Validation Set
+
+The validation set consists of **1,333 images** (20% of the Severstal dataset). Evaluation metric is the **Dice coefficient**.
+
+### Overall Metrics
+
+| Metric | Value |
+|--------|-------|
+| Best overall Dice | **0.6296** |
+| Optimal probability threshold | **0.45** |
+| Best validation loss | 0.4358 (epoch 10) |
+
+### Per‑Class Dice (threshold = 0.45)
+
+| Class | Dice |
+|-------|------|
+| Defect class 1 | 0.651 |
+| Defect class 2 | 0.624 |
+| Defect class 3 | 0.637 |
+| Defect class 4 | 0.606 |
+
+### Threshold Robustness
+
+The Dice score remains between 0.6293 and 0.6296 for thresholds from 0.3 to 0.7, with a maximum at 0.4, 0.45, and 0.5. This indicates that the model produces highly confident predictions (probabilities near 0 or 1).
+
+---
+
+## 🚀 Usage Example (PyTorch)
+
+### Load the model and weights
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Assume you have the model definition from the GitHub repo
+from model import UNetWithResNet34
+
+# Instantiate model
+model = UNetWithResNet34(num_classes=4, pretrained=False)
+model.load_state_dict(torch.load("best.pth", map_location="cpu"))
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((256, 1600)),
+    transforms.ToTensor(),
+])
+
+# Inference
+image = Image.open("steel_sheet.png").convert("RGB")
+input_tensor = transform(image).unsqueeze(0)  # shape: (1, 3, 256, 1600)
+
+with torch.no_grad():
+    logits = model(input_tensor)
+    probs = torch.sigmoid(logits)            # shape: (1, 4, 256, 1600)
+
+# Binarize at optimal threshold
+masks = (probs > 0.45).float()               # shape: (1, 4, 256, 1600)
+Visualise the masks
+python
+import matplotlib.pyplot as plt
+
+# Show class 1 mask
+plt.imshow(masks[0, 0], cmap='gray')
+plt.title("Defect Class 1 Prediction")
+plt.axis('off')
+plt.show()
+📁 Files in this repository
+File	Description
+best.pth	Model weights achieving lowest validation loss (0.4358)
+config.json	(Optional) Training hyperparameters
+README.md	This file
+📝 Notes from the Test Report
+The model successfully learns to detect major defect regions but struggles with small or subtle defects.
+
+Defect sizes vary significantly (small spots to large continuous streaks).
+
+Multiple defect classes can appear on the same image.
+
+The loss curves show no overfitting; further training with stronger augmentation or pseudo‑labeling could improve the Dice score above 0.85.
+
+🔗 Related Resources
+Source code, training scripts, and design documents: GitHub repository
+
+Dataset: Severstal Steel Defect Detection
+
+U‑Net paper: Ronneberger et al., MICCAI 2015
+
+ResNet paper: He et al., CVPR 2016
+
+📄 License
+MIT