---
license: etalab-2.0
pipeline_tag: object-detection
base_model: Ultralytics/YOLO11
---

This model has be trained for the Panoramax project in order to detect:
- people face to blur them
- licence plates to blur them
- road signs to classify them with other models

The last model has been trained on yolo11l with imgsz of 2048 and 300 epochs, the older one on yolo8s.

![](val_batch0_labels.jpg)

Here is the last run validation :
```
Validating runs/detect/train5/weights/best.pt...
Ultralytics 8.3.29 🚀 Python-3.12.3 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4090, 24207MiB)
YOLO11l summary (fused): 464 layers, 25,281,625 parameters, 0 gradients, 86.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 83/83 [00:08<00:00,  9.78it/s]
                   all        329       1209      0.812      0.768      0.815      0.412
                  sign        231        507      0.879      0.836      0.898      0.561
                 plate        202        410      0.833      0.849      0.889      0.438
                  face        118        292      0.724      0.619      0.657      0.237
Speed: 1.2ms preprocess, 18.4ms inference, 0.0ms loss, 1.6ms postprocess per image
```

![](results.png)