Reproduced published accuracies on three popular research benchmark datasets (CARS196, CUB200, and SOP).
Notebook added which shows how to train and evaluate the approaches on a custom dataset.
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.
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