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DeepDetect v0.30.0

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@beniz beniz released this 26 Aug 15:11
· 18 commits to master since this release
v0.30.0
60527bd

Features

  • add external pytorch model import skill (3ec64fc)
  • add tensor-backed pytorch worker dataset adapter (f8c4ec9)
  • added vitpose eval losses (b27951b)
  • additions to pytorch backends to accomodate new detection models (02e43ea)
  • C++ data augmentation wired to input pull connnector (0d85ac4)
  • cli: repository cleansing override (fa0a840)
  • hardening of pytorch backend (622da53)
  • initial keypoint pose estimator input connector (687ac8b)
  • initial pytorch backend structure and messaging layers (c67555c)
  • input c++ connector to pytorch backend pull mode (aa6485f)
  • map equivalence between python worker and C++ implementations (7bc6cdb)
  • model flops estimate (3526835)
  • model flops estimate (d4f6148)
  • model: initial vitpose with hungarian for slot assignment (b703e02)
  • model: regular vitpose with per object keypoints (cec4791)
  • model: RT-DETRv4 for pytorch backend (086bacc)
  • per-class map in backend and python workers metrics (1b44cff)
  • sam2 pytorch worker, inference only (9d340ce)
  • sdk: initial python backend sdk (a4a17e6)
  • sdk: initial torch/torchvision python backend sdk example (bfd862c)
  • torch-only reference detector worker and updated documentation (a20f583)
  • training observability mechanism for agents (993145e)
  • vit patch_embed resampling from timm in vitpose (7c4eae3)
  • vitpose photometric augmentations (f2c30f0)
  • vitpose slot loss control and balancing (51eecb1)

Bug Fixes

  • add frontmatter to DD skill (ec4b612)
  • build with new libs (a29806a)
  • graceful exit with pytorch workers (139a060)
  • inference vitpose slot keypoint coordinates resizing (8594c2c)
  • missing vitpose files (12a859b)
  • pytorch backend map (0be18e1)
  • remove env name from Visdom plot titles so we can compare runs (725a2ac)
  • resume cli when init weights are enabled (31042b3)
  • test metric iterations with pytorch workers (e80791e)
  • vitpose layer wise decay only when backbone is pretrained (135bb4b)
  • wheel building (b903d85)
  • wheels tests (def8918)

Docker images

  • CPU version: docker pull docker.jolibrain.com/deepdetect_cpu:v0.30.0
  • GPU (CUDA only): docker pull docker.jolibrain.com/deepdetect_gpu:v0.30.0
  • GPU (CUDA and TensorRT): docker pull docker.jolibrain.com/deepdetect_gpu_tensorrt:v0.30.0
  • All images are available from https://docker.jolibrain.com/.

Python wheels

Assets 2
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