README.md

metadata

title: ObjectModel-v1 Detection
emoji: 🔍
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 6.24.0
app_file: app.py
short_description: NMS-free COCO object detection with ObjectModel-v1
python_version: '3.12'
startup_duration_timeout: 30m

ObjectModel-v1 — compact NMS-free object detection

Demo of bench-labs/objectmodel-v1, a 40.8M-parameter clean-room object detector trained from scratch on COCO 2017 (AP 0.358 / AP50 0.544 at epoch 95).

The architecture compresses multi-scale pyramid features into a fixed 64-slot latent memory for global semantics, then recovers geometry with query-conditioned local sampling whose radius scales with each query's current box. Prediction is a fixed set of 300 object queries trained with Hungarian matching — no anchors and no NMS.

What this Space does

• Letterboxes the uploaded image to 640×640 (grey padding, ImageNet normalisation), exactly as the upstream evaluation pipeline does.
• Runs a single forward pass of the released EMA checkpoint (objectmodel_v1_best.pt) on a ZeroGPU worker.
• Takes the per-query max-sigmoid score, keeps the top-k above the confidence threshold, and maps boxes back into original image coordinates.
• Renders boxes plus a table of label / score / x0 y0 x1 y1.

Notes

• Small objects are the model's known weak spot (AP_small 0.188 vs AP_large 0.493), and out-of-domain footage degrades faster than COCO-style photography.
• Because there is no NMS, overlapping duplicate boxes are suppressed by the set-prediction training rather than post-processing; raising the confidence threshold is the intended way to clean up marginal detections.

Credits

Model and inference code © Bench Labs, Apache-2.0. The objectmodel_v1/ package in this Space is vendored unchanged from the model repository. Example images come from linoyts/repo-to-space-example-inputs.