Construction-Material Detection Worker (YOLOv8)
RunPod Serverless worker running Ultralytics YOLOv8 object detection for a custom-trained construction-material classifier.
Build & Deploy
- Dockerfile path:
/workers/detect/Dockerfile - Build context: repo ROOT (the COPY paths are prefixed with
workers/detect/). - GPU: light/medium — NVIDIA T4 or A4000 is plenty.
Model
The trained construction-material model (best.pt, 43 MB) is baked into the
image (COPY workers/detect/best.pt → ENV YOLO_MODEL=/app/best.pt), so the
worker detects your materials out of the box — no volume upload needed.
YOLO_MODEL— override only if you want to swap models (default/app/best.pt). To update the model later, replaceworkers/detect/best.ptand push (RunPod rebuilds).
App-side env var
Point your application at the deployed endpoint with:
RUNPOD_YOLO_URL= your RunPod serverless endpoint URL.
Contract
Input:
{ "input": { "image": "<base64>" } }
The base64 may include a data:image/...;base64, prefix; it is stripped.
Output (pixel coordinates):
{ "detections": [
{ "name": "brick", "confidence": 0.94,
"box": { "x1": 10.0, "y1": 20.0, "x2": 110.0, "y2": 220.0 } }
] }
On error: { "error": "<Type>: <message>" }