Yolox has the potential to be applied in a wide range of use cases within the field of computer vision. For instance, it can be used in surveillance systems to detect and track objects in real-time, allowing for improved security and monitoring. Additionally, Yolox can be utilized in autonomous vehicles for object detection and recognition, enabling the vehicle to make informed decisions in complex environments. The model can also find applications in retail, where it can be used to analyze customer behavior and optimize store layouts. Beyond these examples, Yolox can be integrated into various products and services, such as image recognition APIs, mobile applications, and industrial automation systems, to enhance their functionality and efficiency.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Stable Diffusion Speed Lab||$0.0069||3,121|
|Whisper Jax Hindi||$0.0184||62|
|Speedy Stable Diffusion Inpainting||$0.2668||309|
You can use this area to play around with demo applications that incorporate the Yolox model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
High performance and lightweight object detection models
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.01155|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||21 seconds|