yolox
Maintainer: daanelson
16
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
The yolox model is a high-performance and lightweight object detection model developed by Megvii-BaseDetection. It is an anchor-free version of YOLO (You Only Look Once), with a simpler design but better performance. According to the maintainer daanelson, the goal of yolox is to bridge the gap between research and industrial communities.
The yolox model is available in several different sizes, including yolox-s, yolox-m, yolox-l, and yolox-x, which offer a trade-off between performance and model size. For example, the yolox-s model achieves 40.5 mAP on the COCO dataset, while the larger yolox-x model achieves 51.5 mAP but has more parameters and FLOPS.
Other similar object detection models include yolos-tiny and yolo-world. These models take different approaches to object detection, such as using Vision Transformers (yolos-tiny) or focusing on real-time open-vocabulary detection (yolo-world).
Model inputs and outputs
Inputs
- input_image: The path to an image file that the model will perform object detection on.
- model_name: The name of the
yoloxmodel to use, such asyolox-s,yolox-m,yolox-l, oryolox-x. - conf: The confidence threshold for object detections. Only detections with confidence higher than this value will be kept.
- nms: The non-maximum suppression (NMS) threshold. NMS removes redundant detections, and detections with overlap percentage (IOU) above this threshold are considered redundant.
- tsize: The size that the input image will be resized to before being fed into the model.
Outputs
- img: The input image with the detected objects and bounding boxes drawn on it.
- json_str: The object detection results in JSON format, including the bounding boxes, labels, and confidence scores for each detected object.
Capabilities
The yolox model is capable of performing real-time object detection on images. It can detect a wide range of objects, such as people, vehicles, animals, and more. The model's accuracy and speed can be tuned by selecting the appropriate model size, with the larger yolox-x model offering the best performance but requiring more compute resources.
What can I use it for?
The yolox model can be used in a variety of computer vision applications, such as:
- Surveillance and security: The real-time object detection capabilities of
yoloxcan be used to monitor and track objects in surveillance footage. - Autonomous vehicles:
yoloxcan be used for object detection and obstacle avoidance in self-driving car applications. - Robotics: The model can be used to enable robots to perceive and interact with their environment.
- Retail and logistics:
yoloxcan be used for inventory management, shelf monitoring, and package tracking.
Things to try
One interesting aspect of the yolox model is its anchor-free design, which simplifies the object detection architecture compared to traditional YOLO models. This can make the model easier to understand and potentially faster to train and deploy.
Another thing to explore is the different model sizes provided, which offer a trade-off between performance and model complexity. Experimenting with the various yolox models can help you find the right balance for your specific use case.
Additionally, the yolox model supports a variety of deployment options, including MegEngine, ONNX, TensorRT, ncnn, and OpenVINO. Trying out different deployment scenarios can help you optimize the model's performance for your target hardware and application.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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