| --- |
| language: |
| - vi |
| license: agpl-3.0 |
| library_name: ultralytics |
| pipeline_tag: object-detection |
| tags: |
| - yolo |
| - yolov8 |
| - onnx |
| - torchscript |
| - tensorrt |
| - int8 |
| - fp16 |
| - vision |
| - object-detection |
| - traffic-sign |
| - vietnam |
| --- |
| |
| **Documentation languages** |
|
|
| - 🇺🇸 English: README.md |
| - 🇻🇳 Tiếng Việt: README.vi.md |
|
|
| # VTSR |
|
|
| VTSR is a YOLOv8n object-detection model for recognizing 56 classes of Vietnamese traffic signs. |
|
|
| This repository focuses on optimized deployment artifacts derived from the original FP32 PyTorch checkpoint, including ONNX INT8, TensorRT INT8, and TorchScript FP16 models. |
|
|
| ## Demo |
|
|
| An interactive Gradio demo is available on Hugging Face Spaces: |
|
|
| https://huggingface.co/spaces/liamxdev/vtsr |
|
|
| The demo currently uses the ONNX INT8 artifact for CPU-compatible inference. |
|
|
| ## Model details |
|
|
| | Property | Value | |
| | ------------------ | ------------------------------- | |
| | Task | Object detection | |
| | Architecture | YOLOv8n | |
| | Classes | 56 | |
| | Input size | 640 × 640 | |
| | Label descriptions | `label-mapping.json` | |
| | Dataset | Vietnam Traffic Sign Dataset v3 | |
| | License | AGPL-3.0 | |
|
|
| The model returns traffic-sign codes such as `P-102`, `R-303`, and `W-224`. Vietnamese descriptions for these codes are provided separately in `label-mapping.json`. |
|
|
| ## Artifacts |
|
|
| | File | Precision | Target platform | |
| | ----------------------- | --------- | -------------------------------------------- | |
| | `vtsr.pt` | FP32 | PyTorch | |
| | `vtsr_int8.onnx` | INT8 | ONNX Runtime, CPU, cross-platform deployment | |
| | `vtsr.torchscript` | FP16 | TorchScript runtimes | |
| | `vtsr.engine` | INT8 | NVIDIA TensorRT and Jetson devices | |
| | `label-mapping.json` | — | Traffic-sign code descriptions | |
|
|
| > The original FP32 PyTorch checkpoint is intentionally omitted. This repository provides optimized deployment artifacts only. |
|
|
| ## Usage |
|
|
| Install dependencies: |
|
|
| ```bash |
| pip install ultralytics |
| ``` |
|
|
| Run inference: |
|
|
| ```python |
| from ultralytics import YOLO |
| |
| # Supported artifacts: |
| # vtsr.pt |
| # vtsr_int8.onnx |
| # vtsr.torchscript |
| # vtsr.engine |
| |
| model = YOLO("vtsr_int8.onnx") |
| |
| results = model.predict( |
| source="traffic.jpg", |
| imgsz=640, |
| conf=0.25, |
| ) |
| |
| results[0].save("result.jpg") |
| ``` |
|
|
| See `label-mapping.json` for Vietnamese descriptions of traffic-sign codes. |
|
|
| ## Download from Hugging Face Hub |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| repo_id = "liamxdev/vtsr" |
| |
| model_path = hf_hub_download( |
| repo_id=repo_id, |
| filename="vtsr_int8.onnx", |
| ) |
| |
| mapping_path = hf_hub_download( |
| repo_id=repo_id, |
| filename="label-mapping.json", |
| ) |
| ``` |
|
|
| Other artifacts can be downloaded in the same way: |
|
|
| * `vtsr.pt` |
| * `vtsr.torchscript` |
| * `vtsr.engine` |
|
|
| ## Benchmark |
|
|
| See [benchmark.md](benchmark.md) for latency and throughput measurements. |
|
|
| ## Supported classes |
|
|
| The model covers 56 Vietnamese traffic-sign codes in four broad groups: |
|
|
| * `P-*`: prohibition signs. |
| * `R-*`: mandatory and instruction signs. |
| * `W-*`: warning signs. |
| * `S-*` and `DP-*`: supplementary or special signs represented in the training dataset. |
|
|
| See `label-mapping.json` for the complete list and Vietnamese meanings. |
|
|
| ## Training data |
|
|
| The model was trained using version 3 of the [Vietnam Traffic Sign Dataset](https://universe.roboflow.com/giang-yp9g1/vietnam-traffic-sign-altsi/dataset/3) published on Roboflow Universe. The dataset metadata declares a CC BY 4.0 license. |
|
|
| ## Intended use |
|
|
| * Educational and research projects. |
| * Traffic-scene analysis prototypes. |
| * Vietnamese traffic-sign detection demos. |
| * CPU-oriented deployment through ONNX Runtime. |
| * NVIDIA GPU and Jetson deployment through TensorRT. |
| * Lightweight deployment through TorchScript. |
|
|
| ## Limitations |
|
|
| * The model recognizes only the 56 classes listed in `label-mapping.json`. |
| * Detection quality can decrease with small, blurred, occluded, damaged, poorly lit, or unusually angled signs. |
| * Images outside the training distribution may produce missed detections or false positives. |
| * A detection is not a substitute for official road-sign interpretation or safety-critical driving decisions. |
| * Quantized and reduced-precision models may exhibit accuracy differences relative to the original PyTorch checkpoint. |
| * TensorRT engines are hardware-specific and may need to be regenerated for different GPU architectures. |
|
|
| ## License |
|
|
| The exported model contains Ultralytics YOLO components and is distributed under the AGPL-3.0 license. Dataset attribution and its CC BY 4.0 terms apply separately. |
|
|
| For commercial licensing of Ultralytics components, see https://www.ultralytics.com/license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{vietnam_traffic_sign_altsi_dataset, |
| title = {Vietnam Traffic Sign Dataset}, |
| author = {giang}, |
| year = {2024}, |
| publisher = {Roboflow Universe}, |
| howpublished = {\url{https://universe.roboflow.com/giang-yp9g1/vietnam-traffic-sign-altsi}} |
| } |
| ``` |
|
|
