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🤘 TT-NN operator library, and TT-Metalium low level kernel programming model.
A curated list of research papers, datasets, and tools for applying machine learning/Deep learning techniques to compilers and program optimization.
IDE style command line auto complete
Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
A simple, fast and user-friendly alternative to 'find'
ripgrep recursively searches directories for a regex pattern while respecting your gitignore
Additions and patches to Caffe framework for use with Synopsys DesignWare EV Family of Processors
PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
A real-time object detection framework of Yolov3/v4 based on caffe
🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
A collection of SOTA Image Classification Models in PyTorch
Reinforcement learning environments for compiler and program optimization tasks
Google Research
You Only Look Once for Panopitic Driving Perception.（MIR2022）
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation
C++ tensors with broadcasting and lazy computing
CVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite