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Releases: microsoft/computervision-recipes

Release version 1.2

10 Jul 20:54
6608a90
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Highlights

Scenarios added

Tracking:

  • Added state-of-the-art support for multi-object tracking based on the FairMOT approach described in the 2020 paper "A Simple Baseline for Multi-Object Tracking".
  • Reproduced published accuracies on the popular MOT research benchmark datasets.
  • Notebooks for training using a custom dataset, and for reproducing results on the MOT dataset.

Action Recognition

  • Added state-of-the-art support for action recognition from video based on the R(2+1)D approach described in the 2019 paper "Large-scale weakly-supervised pre-training for video action recognition".
  • Reproduced published accuracies on the popular HMDB-51 research benchmark dataset.
  • Notebooks for training using a custom dataset, and for reproducing results on the HMDB-51 dataset.

Release version 1.1

27 Mar 13:52
e3b455a
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Highlights

Scenarios added or expanded

Similarity:

Detection:

  • Added Mask-RCNN functionality to detect and segment objects.
  • Added speed vs. accuracy trade-off analysis using the COCO dataset for benchmarking.
  • Improved visualization of e.g. predictions, ground truth, or annotation statistics.
  • Notebooks added which show how to: (i) run and train a Mask-RCNN model; (ii) evaluate on the COCO dataset; (iii) perform active learning via hard-negative sampling.

Keypoint:

  • New scenario.
  • Notebook added which shows: (i) how to run a pre-trained model for human pose estimation; and (ii) how to train a keypoint model on a custom dataset.

Action (in 'contrib' folder):

Computer Vision Repo 2019.09

30 Sep 17:09
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Pre-release

Scenarios

Classification:

  • Introduction notebooks that include the basics of training a cutting edge classification model, how to do multi-label classification, and evaluating speed vs accuracy
  • Advanced topic notebooks that include hard-negative mining, and basic exploration of parameters
  • Notebooks that show how to use Azure ML to operationalize your model, and Azure ML Hyperdrive to perform exhaustive testing on your model

Similarity:

  • Introduction notebooks that performs basic training and evaluation for image similarity
  • Notebooks that show how to use Azure ML hyperdrive to perform exhaustive testing on your model

Detection:

  • Introduction notebooks that performs basic training and evaluation for object detection
  • Notebooks that show how to use Azure ML hyperdrive to perform exhaustive testing on your model