Yolo Semantic Parking
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.
Model source
- Kind
- Other
- File size
- 5.59 MB
- Version
- vRevision 00b4f19
- Updated
- Sep 27, 2026
- Downloads
- 0
- Tasks
- Object detection
- Source
- Hugging Face
Source description
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.
Comparison
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 |
|---|---|
| baseline | semantic |
Confusion Matrix
| Baseline | Semantic v4 |
|---|---|
| baseline cm | semantic cm |
Semantic Alignment Visualization
| Epoch 20 | Epoch 30 | Epoch 40 | Epoch 49 |
|---|---|---|---|
| e20 | e30 | e40 | e49 |
semantic space
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
Sources
1 source
Sources
1 sourceVerified Sep 27
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- 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...
Source context: 0 downloads · 0 likes · Pipeline object-detection · Repo Alamoudi/yolo-semantic-parking
