DeepDetect v0.20.0

Features

• feat: add elapsed time to training metrics (fe5fc41)
• feat: add onnx export for torchvision models (07f69b1)
• feat: add yolox export script for training and inference (0b2f20b)
• feat: add yolox onnx export and trt support (80b7e6a)
• api: chain uses dto end to end (5efbf28)
• ml: data augmentation for training segmentation models with torch backend (b55c218)
• ml: DETR export and inference with torch backend (1e4ea4e)
• feat: full cuda pipeline for tensorrt (93815d7)
• ml: noise image data augmentation for training with torch backend (2d9757d)
• ml: training segmentation models with torch backend (1e3ff16)
• ml: activate cutout for object detector training with torch backend (8a34aa1)
• ml: distortion noise for image training with torch backend (35a16df)
• ml: dice loss https://arxiv.org/abs/1707.03237 (542bcb4)
• ml: manage models with multiple losses (bea7cb4)

Bug Fixes

• cpu: cudnn is now on by default, auto switch it to off in case of cpu_only (3770baf)
• tensorrt: read onnx model to find topk (5cce134)
• simsearch ivf index craft after reload, disabling mmap (8a2e665)
• tensorrt: yolox postprocessing in C++ (1d781d2)
• torch: add include sometimes needed (74487dc)
• add mltype in metrics.json even if training is not over (9bda7f7)
• clang formatting of mlmodel (130626b)
• torch: avoid crashes caused by an exception in the training loop (667b264)
• torch: bad bbox rescaling on multiple uris (05451ed)
• torch: correct output name for onnx classification model (a03eb87)
• torch: prevent crash during training if an exception is thrown (4ce7802)

Docker images:

• CPU version: docker pull jolibrain/deepdetect_cpu:v0.20.0
• GPU (CUDA only): docker pull jolibrain/deepdetect_gpu:v0.20.0
• GPU (CUDA and Tensorrt) :docker pull jolibrain/deepdetect_cpu_tensorrt:v0.20.0
• GPU with torch backend: docker pull jolibrain/deepdetect_gpu_torch:v0.20.0
• All images available on https://hub.docker.com/u/jolibrain