metadata
license: apache-2.0
tags:
- object-detection
- computer-vision
- yolox
- yolov3
- yolov5
datasets:
- detection-datasets/coco
Model Description
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported.
YOLOXDetect-Pip: This repo is a packaged version of the YOLOX for easy installation and use.
[Paper Repo]: Implementation of paper - YOLOX
Installation
pip install yoloxdetect
Yolov6 Inference
from yoloxdetect import YoloxDetect
from yolox.data.datasets import COCO_CLASSES
model = YoloxDetect(
model_path = "kadirnar/yolox_s-v0.1.1",
config_path = "configs.yolox_s",
device = "cuda:0",
classes = COCO_CLASSES,
confidence_threshold = 0.25,
nms_threshold = 0.45,
)
model.classes = COCO_CLASSES
model.conf = 0.25
model.iou = 0.45
model.show = False
model.save = True
pred = model.predict(image='data/images', img_size=640)
BibTeX Entry and Citation Info
@article{yolox2021,
title={YOLOX: Exceeding YOLO Series in 2021},
author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2107.08430},
year={2021}
}