One of the most well-known one-stage object detection techniques is YOLOX, which is often utilized in various fields, including automated driving and defect checking. It adds a decoupled head and an anchor-free method to the YOLO series and gets cutting-edge performance between 40 and 50 mAP. Researchers want to incorporate YOLOX into their EasyCV. This all-in-one computer vision approach enables a newbie to quickly employ a computer vision algorithm due to its adaptability and effectiveness. Additionally, they examine YOLOX’s potential for advancement by employing various neck, head, and backbone enhancements.
Users only need to choose various configurations to get an appropriate object detection model that meets their needs. They also speed up the inference process based on PAI-Blade 1, an inference optimization framework from PAI, and offer a simple API to use PAI-Blade in their EasyCV. Finally, they create an effective predictive API to utilize their YOLOX-PAI end-to-end, greatly accelerating the original YOLOX. In the figure below comparisons between YOLOX-PAI and cutting-edge object identification techniques are displayed.
Briefly stated, the following are their significant contributions:
• Yolox-PAI, a straightforward yet effective object identification tool, is now available in EasyCV (containing the docker image, the process of model training, model evaluation, and model deployment). their YOLOX-PAI is designed to be user-friendly so that anybody may complete object detection tasks.
• Using a configuration file to create a self-designed YOLOX model, They undertake ablation investigations of the current object identification methods.
• In EasyCV, they offer a flexible prediction API that speeds up the preprocessing, inference, and postprocessing processes. This will improve the user’s ability to use YOLOX-PAI for end-to-end objection detection tasks.
They achieve state-of-the-art object identification performances between 40 mAP and 50 mAP within 1 ms for model inference on a single NVIDIA Tesla V100 GPU, thanks to the architecture and PAI efficiency improvements.
Especially in the areas of self-supervised learning and vision transformer, EasyCV is an all-in-one toolkit box that focuses on SOTA computer vision techniques. A better version of YOLOX based on EasyCV is called YOLOX-PAI. The results of SOTA object recognition between 40 and 50 mAP are received thanks to PAI-Blade and model architecture improvements. Additionally, They provide a simple and effective prediction API for configurable end-to-end object identification. They anticipate that customers will be able to utilize EasyCV to execute computer vision activities immediately and enjoy CV.
Some of the Major features of EasyCV are:
- ASL SOTA Algorithms
Modern self-supervised learning techniques, such as SimCLR, MoCO V2, Swav, DINO, and MAE based on masked image modeling, are available through EasyCV.They also offer standard benchmarking tools for evaluating SSL models.
- Transformers for the eyes
EasyCV intends to make it simple to employ pre-trained SOTA transformer models like ViT, Swin Transformer, and DETR Series that were either trained using supervised learning or self-supervised learning. They also support all of the timm’s pretrained models. Future updates will include more models.
- Extensibility & Function
Along with SSL, EasyCV also offers object identification, metric learning, picture classification, and more fields that will be supported in the future. EasyCV offers an intuitive interface with a wealth of features for inference. Despite addressing a variety of topics, EasyCV breaks down the framework into distinct elements like the dataset, model, and running hook, making it simple to add new elements and integrate them with already-existing modules. Additionally, PAI-EAS, which supports automated scaling and service monitoring and is simple to implement as an online service, supports all models.
EasyCV supports training with multiple GPUs and workers. EasyCV outputs a model using a jit script for inference optimization, which PAI-Blade can optimize. DALI is used by EasyCV to speed up data IO and to preprocess, whereas TorchAccelerator and fp16 are used to speed up training.
The EasyCV toolkit is open source and available on GitHub.
This Article is written as a research summary article by Marktechpost Staff based on the research paper 'YOLOX-PAI: An Improved YOLOX Version by PAI'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and github link. Please Don't Forget To Join Our ML Subreddit
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