Object Detection
ultralytics
ONNX
TensorRT
Vietnamese
yolo
yolov8
torchscript
int8
fp16
vision
traffic-sign
vietnam
Instructions to use liamxdev/vtsr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use liamxdev/vtsr with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("liamxdev/vtsr") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - TensorRT
How to use liamxdev/vtsr with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Environment
- Platform: Google Colab
- GPU: NVIDIA Tesla T4
- Input size: 640×640
- Batch size: 1
- Warm-up runs: 30
- Measured runs: 200
Results
| Artifact | Mean Latency (ms) | Median Latency (ms) | P95 Latency (ms) | FPS (Median) |
|---|---|---|---|---|
| ONNX INT8 | 733.704 | 634.253 | 1196.094 | 1.58 |
| TorchScript FP16 | 15.526 | 15.174 | 17.666 | 65.90 |
| TensorRT INT8 | 12.956 | 12.774 | 14.836 | 78.28 |
TensorRT INT8 achieved the best latency and throughput on an NVIDIA Tesla T4 GPU. TorchScript FP16 delivered comparable performance, while the ONNX INT8 artifact showed substantially higher latency in this environment.