DALI
soft_nms
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DALI
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[D] Will data augmentations work faster on TPUs?
Another option is DALI https://github.com/NVIDIA/DALI For my project while training EfficientNet2, it was a game changer. But it a way harder to implement in code than TorchVision or Kornia.
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DirectStorage - Loading data to GPU *directly* from the SSD drive, almost without using CPU
Check out https://github.com/nvidia/DALI
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mmap_ninja: Speedup your training dramatically by using memory-mapped files for your dataset
Small question if you are using GPU: How to this compare to GPUDirect Storage from Nvidia? can you have even more speedup by using both? I never toy with it, but the DALI project from Nvidia seem to tackle the same data loading problem.
- [D] Efficiently loading videos in PyTorch without extracting frames
soft_nms
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[P] Soft-nms in Pytorch
It can be found here: https://github.com/MrParosk/soft_nms
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Blurry - Blurry is an easy blur library for Android
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
vision - Datasets, Transforms and Models specific to Computer Vision
mmdetection - OpenMMLab Detection Toolbox and Benchmark
executorch - On-device AI across mobile, embedded and edge for PyTorch
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
DREAMPlace - Deep learning toolkit-enabled VLSI placement
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
MegEngine - MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
Open3D - Open3D: A Modern Library for 3D Data Processing