Lenta Tech Life 2026 Detector

--- license: agpl-3.0 library_name: ultralytics pipeline_tag: object-detection base_model: openfoodfacts/price-tag-detection tags: - object-detection - yolo11 - yolov11 - price-tag - retail - lenta-tech-life-2026 --- # Lenta Tech Life 2026 — Price-Tag Detector Fine-tuned **YOLO11x** that detects supermarket price tags in frames captured by a shelf-scanning robot. It is the detection stage of an end-to-end price-tag recognition pipeline (detect → track → dedup → read fields → CSV) built for the **Lenta Tech Life 2026** hackathon. ## Model - **Architecture:** Ultralytics YOLO11x (single class: `price_tag`). - **Base:** [`openfoodfacts/price-tag-detection`](https://huggingface.co/openfoodfacts/price-tag-detection) (itself a YOLO11 model), fine-tuned for the Lenta robot camera. - **Trained upright.** The robot camera is mounted rotated 90° CCW; the detector is trained on de-rotated (upright) frames, so the runtime must feed it upright frames (the pipeline config sets `frame_rotation: ccw` for offline clips; uploaded clips are already upright). ## Training data Fine-tuned on the provided Lenta dataset (494 frames) plus external open-license datasets auto-labeled with open-source models, under strong camera-matched augmentation (motion blur, glare, perspective, wide-angle distortion) to match the robot's ultra-wide 2.8 mm lens. No cloud or online services are used at inference — the whole pipeline runs locally. ## Usage The pipeline resolves this model automatically from its config via an `hf://` reference — no manual download needed for `docker compose up`: ```yaml detector: model_path: hf://BlackfireZZZ/lenta-tech-life-2026-detector/lenta_price_tag_detector_full494_off_aug_best.pt ``` Or load it directly with Ultralytics: ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO p = hf_hub_download( "BlackfireZZZ/lenta-tech-life-2026-detector", "lenta_price_tag_detector_full494_off_aug_best.pt", ) model = YOLO(p) ``` ## License **AGPL-3.0**, inherited from Ultralytics YOLO11 and the OpenFoodFacts base model. Any deployment must comply with AGPL-3.0.