Try this model on one of your photos to see its result here.

Pill blister inspection

Inspects blister packs: finds each pill and each empty pocket, front and back.

Specifications

Training data: pill, from the RF100-VL benchmark
Accuracy: Boxes
mAP50-95
0.728
mAP50
0.979
mAP75
0.830
Precision
98.3%
Recall
98.4%
F1
98.4%
Held-out validation subset: 58 photos, 829 objects (a class-balanced 20% of the validation split). Precision, recall and F1 at confidence 0.30, IoU 0.5 · measured Nov 9, 2026
Boxes, whole benchmark
mAP50-95
0.697
RF100-VL benchmark, all 100 datasets · measured Nov 10, 2026
Validate this model yourself on /detect
Image requirements: JPG, PNG or WebP, up to 10 MB. The model works at 1280 px and expects photos like the example: it was trained on this kind of image only.
Video requirements: Photos only.
Input / output: In: one image, multipart/form-data fields file and project_id. Out: JSON detections, each with class and bbox [x1, y1, x2, y2] in pixels.
Licence: Use through the Comexp API under our Terms of service.
Version: Not stated
Hardware: Runs in the Comexp cloud: nothing to install, any device that can send an HTTP request. On your own servers or devices: on request.
API: curl -X POST https://ml.comexp.net/api/detect \ -H "Authorization: ApiKey <YOUR_API_KEY>" \ -F "project_id=tape_pill_blister" \ -F "file=@image.jpg"
Each call costs one detection from your plan, as on /detect. Your API key is in your account settings.