README.md

metadata

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:

pip install ultralytics

Run inference:

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

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 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 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

@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}}
}