YOLOv8 Architectural Image Segmentation
Budget: $15 – $25 AUD
I need a production-ready image-segmentation pipeline that cleanly separates structural elements—walls, doors, windows, stairs—from full-sheet architectural plans. The model must run server-side and expose a lightweight REST or GraphQL endpoint so my web application can request a plan, receive masks or overlaid PNG/SVG layers, and continue its own post-processing.
My current stack is Python, FastAPI, and Docker, so training in PyTorch with Ultralytics YOLOv8 (or a demonstrably better architecture) will slot in perfectly. You can expect hundreds of high-resolution TIFF and PDF drawings; I will supply a curated, annotated subset at project start. The rest of the dataset may need additional labeling, so please account for best-practice augmentation, tiling, and class-imbalance handling.
Deliverables:
• Trained segmentation model (weights + config) achieving reliable IOU on walls, doors, windows, stairs and “other”.
• Inference script wrapped in FastAPI, docker-ised, accepting base64 or URL, returning JSON masks plus optional overlay image.
• Brief README covering environment setup, training commands, and endpoint usage.
Acceptance is straightforward: I will run your container on a fresh GPU instance and test on 50 unseen plans; masks must reach the agreed IOU threshold and return in under 3 s per plan. Any questions about the drawings or classes—let’s clarify early so the first training cycle is already on the right track.
My current stack is Python, FastAPI, and Docker, so training in PyTorch with Ultralytics YOLOv8 (or a demonstrably better architecture) will slot in perfectly. You can expect hundreds of high-resolution TIFF and PDF drawings; I will supply a curated, annotated subset at project start. The rest of the dataset may need additional labeling, so please account for best-practice augmentation, tiling, and class-imbalance handling.
Deliverables:
• Trained segmentation model (weights + config) achieving reliable IOU on walls, doors, windows, stairs and “other”.
• Inference script wrapped in FastAPI, docker-ised, accepting base64 or URL, returning JSON masks plus optional overlay image.
• Brief README covering environment setup, training commands, and endpoint usage.
Acceptance is straightforward: I will run your container on a fresh GPU instance and test on 50 unseen plans; masks must reach the agreed IOU threshold and return in under 3 s per plan. Any questions about the drawings or classes—let’s clarify early so the first training cycle is already on the right track.