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Verified signature--- license: mit tags: - semantic-segmentation - computer-vision - unet - efficientnet - road-segmentation - tensorflow - keras datasets: - custom - camvid metrics: - mean_iou --- # Road Scene Semantic Segmentation A semantic segmentation model for autonomous driving scenes, built with EfficientNetB5 + UNet decoder + ASPP module. The model segments road scenes into 5 classes: background, road surface, road markings, road signs, and cars. ## Model Architecture The model combines a pretrained **EfficientNetB5** encoder with a custom **UNet-style decoder** and an **ASPP (Atrous Spatial Pyramid Pooling)** module at the bottleneck. This design captures both high-level semantics and fine-grained spatial details — particularly useful for thin structures like lane markings. ``` Input (256×256×3) │ EfficientNetB5 Encoder (pretrained on ImageNet) ├── skip connections at multiple resolutions └── bottleneck feature maps │ ASPP Module ├── Conv 1×1 ├─…d16b92c -
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