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Cheetah

Cheetah is an optimization zoo for vision transformer (ViT) that consists of all popular ViT optimization techniques (sparse, pruning and multi-exit). Prior to our implementation, I list all influencing papers about ViT acceleration and develop them as optimization modules for any ViT models. Unlike FasterTransformer and DeepSpeed, Cheetah pays more attention to vision transformer instead of BERT, and provids more developer-friendly code through modular design (users can use their favorite optimizations to accelerate ViT). In the end, I will introduce how to profile your new ViT models and deploy them to NVIDIA Trition server (including how to accelerate them with TensorRT).

ViT Models

<!-- In the end, we also provide codes for speeding up training and inference on Pytorch and GPU (CUDA). --> <!-- ## Installation and Documentation (Finished) --> <!-- ### Docker --> <!-- ### Triton Installation (Docker mode) --> <!-- ### Kubernetes (for scalability) --> <!-- ## Code (TBD) --> <!-- ## Paper (In progress) --> <!-- ### Image Classification --> <!-- ### Object Detection (In progress) --> <!-- 1. [YOLOv4: Optimal Speed and Accuracy of Object Detection. In arXiv'2020.](https://arxiv.org/abs/2004.10934) [Official Code by Darknet (C)](https://github.com/AlexeyAB/darknet#how-to-use-on-the-command-line) 2. [YOLOv3: An Incremental Improvement. In arXiv'2018.](https://arxiv.org/abs/1804.02767) [Official Code by Darknet (C)](https://github.com/AlexeyAB/darknet#how-to-use-on-the-command-line) / [Unofficial Code by Pytorch (Python)](https://github.com/ultralytics/yolov3) - the most popular model in real-time object detection. ### Segmentation ### Tracking <!-- #### Fast training and inference --> <!-- ##### CPU --> <!-- ##### GPU --> <!-- ### Useful Tricks --> <!-- 1. [Computation Reallocation for Object Detection. In ICLR'20.](https://iclr.cc/virtual_2020/poster_SkxLFaNKwB.html) --> -->

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