Upload initial DeepLabV3 ResNet50 segmentation model with corrected README.md

README.md

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+
+---
+base_model: deeplabv3_resnet50
+model_name: offroad_segmentation
+tags:
+- image-segmentation
+- pytorch
+- computer-vision
+- deeplabv3
+widget:
+  - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/widget-images/image-segmentation.png
+    example_title: Image segmentation example
+---
+# Offroad Terrain Segmentation Model
+
+This is a semantic segmentation model trained to identify offroad terrains from images.
+
+## Model Details
+- **Model Architecture**: DeepLabV3 with ResNet50 backbone
+- **Pre-training**: Initialized with weights pre-trained on COCO dataset
+- **Dataset**: 'Offroad_Segmentation_Training_Dataset'
+- **Input**: RGB images (540x960 pixels)
+- **Output**: Segmentation mask with 2 classes (e.g., background, offroad terrain)
+- **Training Epochs**: 10
+- **Batch Size**: 16
+- **Learning Rate**: 0.001
+
+## Training Metrics
+- **Final Training Loss**: 0.0682
+- **Final Validation Loss**: 0.0785
+- **Final Training Mean IoU**: 0.2789
+- **Final Validation Mean IoU**: 0.2690
+
+## How to use
+
+```python
+import torch
+import torchvision.models.segmentation as models
+from torchvision import transforms
+from PIL import Image
+import cv2
+import numpy as np
+
+# Load the model architecture
+model = models.deeplabv3_resnet50(pretrained=False) # Set pretrained=False as we load custom weights
+model.classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=1) # Adjust output channels for 2 classes
+
+# Load the state dictionary
+model_path = "deeplabv3_resnet50_offroad.pth" # Path to your saved model
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+model.eval()
+
+# Preprocessing transformations
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def predict_mask(image_path, model, transform):
+    image = Image.open(image_path).convert("RGB")
+    original_size = image.size
+
+    # Resize to model's expected input size (960x540 for this model, or handle dynamic resizing)
+    # For simplicity, assuming model input was trained on fixed size, let's resize
+    image_tensor = transform(image).unsqueeze(0) # Add batch dimension
+
+    with torch.no_grad():
+        output = model(image_tensor)['out']
+
+    # Get the predicted class for each pixel
+    predicted_mask = torch.argmax(output.squeeze(), dim=0).cpu().numpy()
+
+    # Resize mask back to original image size if necessary
+    predicted_mask_resized = cv2.resize(predicted_mask.astype(np.uint8), original_size, interpolation=cv2.INTER_NEAREST)
+
+    return predicted_mask_resized
+
+# Example usage:
+# Assuming you have an image 'test_image.jpg'
+# mask = predict_mask('test_image.jpg', model, transform)
+# plt.imshow(mask)
+# plt.show()
+```

deeplabv3_resnet50_offroad.pth

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