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yolo-semantic-parking
Alamoudi/yolo-semantic-parking
yolo-semantic-parking is a object detection model from Alamoudi. Use it when you need objects located in an image. The card lists the license as agpl-3.0.
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.
Access
Public
Updated May 19, 2026
Repo size
5.9 MB
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.pt5.9 MB · 98%
At a glance
- Task
- Object Detection
- License
- agpl-3.0
- Access
- Public
- Created
- May 19, 2026
- Updated
- May 19, 2026
- SHA
- 00b4f19d
Model card
From the Hugging Face model README
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
from ultralytics import YOLO
model = YOLO('best.pt')
results = model('your_image.jpg')
Training Config
tau=0.1— InfoNCE temperaturesem_weight=0.2— semantic loss weightsem_warmup=10— epochs before semantic loss activates- 50 epochs, batch=16, imgsz=640, A100 40GB
Links
- Task
- Object Detection
- License
- agpl-3.0
- Created
- May 19, 2026
- Updated
- May 19, 2026