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YOLO11s model trained on the Vietnam Traffic Sign Detection dataset.
Modellquelle
Quellen
1 QuelleVerifiziert 9. Sept.
Modellartefakte
2 ArtefakteQuellenauszüge
2 Auszüge| Property | Value |
|---|
| Model | YOLO11s |
| Classes | 82 Vietnamese traffic signs |
| Image size | 640×640 |
| Framework | Ultralytics |
| Dataset Source | Hugging Face |
Below is the summary of the evaluation results from evalution/summary.json:
| Metric | Value |
|---|---|
| Model | yolo11s.pt |
| Epochs Trained | 50 |
| Number of Classes | 82 |
| Device | GPU |
| Precision | 96.42% (0.9642) |
| Recall | 96.15% (0.9615) |
| mAP50 | 98.06% (0.9806) |
| mAP75 | 93.37% (0.9337) |
| mAP50-95 | 83.57% (0.8357) |
| FPS | 61.5 |
| Mean Latency |
Training Curves
Results
Random Predictions
| File | Description |
|---|---|
best.pt | PyTorch weights (main model) |
best.onnx | ONNX export (CPU/edge deploy) |
data.yaml | Dataset config with class names |
config.json | Training hyperparameters |
metrics.json | Test-set evaluation results |
benchmark.json | FPS / latency results |
summary.json | All metrics combined |
from ultralytics import YOLO
# PyTorch
model = YOLO("best.pt")
results = model("image.jpg", conf=0.25)
results[0].show()
# ONNX (faster on CPU)
model_onnx = YOLO("best.onnx")
results = model_onnx("image.jpg")
python infer.py --source image.jpg
python infer.py --source video.mp4 --save
python infer.py --source 0 --show # webcam
--- tags: - object-detection - ultralytics - yolo - traffic-sign - vietnam - yolo11s library_name: ultralytics license: apache-2.0 datasets: - traffic-sign-detection-vietnam --- # 🚦 Traffic Sign Detection — Vietnam (YOLO11s) [](https://github.com/ultralytics/ultralytics) [](https://huggingface.co/datasets/star092304/Traffic-sign-detection-VietNam) YOLO11s model trained on the Vietnam Traffic Sign Detection dataset. | Property | Value | |----------|-------| | Model | YOLO11s | | Classes | 82 Vietnamese traffic signs | | Image size | 640×640 | | Framework | Ultralytics | | Dataset Source | [Hugging Face](https://huggingface.co/datasets/star092304/Traffic-sign-detection-VietNam) | ## Evaluation Results Below is the summary of the evaluation results from `evalution/summary.json`: | Metric | Value | |--------|-------| | **Model** | `yolo11s.pt` | | **Epochs Trained** | 50 | | **Number of Classes** | 82 | | **Device** | GPU | | **Precision** | 96.42% (`0.9642`) | | **Recall** | 96.15% (`0.9615`) | | **mAP50** | 98.06% (`0.9806`) | | **mAP75** | 93.37% (`0.9337`) | | **mAP50-95** | 83.57% (`0.8357`) | | **FPS** | 61.5 | | **Mean Latency** | 16.25 ms | | **p50 Latency** | 15.03 ms | | **p95 Latency** | 22.59 ms | | **Min Latency** | 13.44 ms | | **Max Latency** | 23.24 ms | ## Visualizations ### Training Curves  ### Results  ### Random Predictions (Inference)  ## Files | File | Description | |------|-------------| | `best.pt` | PyTorch weights (main model) | | `best.onnx` | ONNX export (CPU/edge deploy) | | `data.yaml` | Dataset config with class names | | `config.json` | Training hyperparameters | | `metrics.json` | Test-set evaluation results | | `benchmark.json` | FPS / latency results | | `summary.json` | All metrics combined | ## Quick Start ```python from ultralytics import YOLO # PyTorch model = YOLO("best.pt") results = model("image.jpg", conf=0.25) results[0].show() # ONNX (faster on CPU) model_onnx = YOLO("best.onnx") results = model_onnx("image.jpg") ``` ## CLI Inference ```bash pytho...
Source context: 40 downloads · 2 likes · Pipeline object-detection · Library ultralytics · Repo star092304/traffic-sign-detection-vietnam-yolo
| 16.25 ms |
| p50 Latency | 15.03 ms |
| p95 Latency | 22.59 ms |
| Min Latency | 13.44 ms |
| Max Latency | 23.24 ms |