Yolo Semantic Parking

--- license: agpl-3.0 tags: - object-detection - yolo - clip - semantic-conditioning - parking --- # YOLO Semantic Conditioning — Parking Slot Detection YOLO26n fine-tuned with **CLIP-based semantic conditioning** on a parking slot dataset. Text descriptions attached to bounding boxes guide neck features via InfoNCE contrastive loss.  --- ## Results | Model | mAP50 | mAP50-95 | |---|---|---| | Baseline (no semantic) | 0.872 | 0.663 | | **This model (semantic_v4)** | **0.890** | **0.660** | +0.018 mAP50 over equal-epoch baseline. ### Training Curves | Baseline | Semantic v4 | |---|---| |  |  | ### Confusion Matrix | Baseline | Semantic v4 | |---|---| |  |  | --- ## Semantic Alignment Visualization | Epoch 20 | Epoch 30 | Epoch 40 | Epoch 49 | |---|---|---|---| |  |  |  |  |  --- ## Usage ```python from ultralytics import YOLO model = YOLO('best.pt') results = model('your_image.jpg') ``` ## Training Config - `tau=0.1` — InfoNCE temperature - `sem_weight=0.2` — semantic loss weight - `sem_warmup=10` — epochs before semantic loss activates - 50 epochs, batch=16, imgsz=640, A100 40GB --- ## Links - Code & full det...