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Fast-SCNN Floor Segmentation
Lightweight Fast-SCNN model for indoor floor segmentation.
- Task: Semantic Segmentation
- Class: Floor
- Input: RGB
512×512 - Pixel Accuracy: 94.9%
- mIoU: 87.8%
Model Formats
- PyTorch:
.pth - ONNX:
.onnx - OpenVINO:
.xml + .bin
Performance
OpenVINO FP32: 26.02 FPS / 38.44 ms per frame on Intel Core 7 240H.
Limitations
Designed for indoor floor segmentation. Performance may degrade with unseen environments, lighting changes, shadows, furniture, and floor-like surfaces.
The model detects floor only and does not perform obstacle detection.
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