Single responsibility containers for data science and machine learning to create end-to-end machine learning pipelines on Azure Kubernetes Service
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Containers
Infrastructure
Pipeline
.gitignore
InstanceSegmentationPipeline.jpg
README.md

README.md

Instance Segmentation Pipeline - Mask R-CNN, PyTorch

Easily create custom computer vision models to detect and mask objects in your images and video for counting, movement, shape, and proportions.

Instance Segmentation Pipeline

  1. Frame Reduction and Proposal - From a video, retain only the frames consisting of the visual content you're interested in, using the Cognitive Services, Scene and Activity Recognition Service.
  2. Frame Annotation - Annotate frames using VGG Image Annotator, COCO Annotation UI, Labelbox or another image annotation tool.
  3. Visualise Class Distribution - View the accumulation of samples per class. Extend this notebook to describe frames using custom metadata for subsetting.
  4. Prepare Training Dataset - Create Training and Validation sets using metadata to subset.
  5. View Training Dataset - View the Training and Validation sets - and right any wrongs from annotating frames - prior to model training.
  6. Model Training - Let the magic begin. Be sure to specify the Training and Validation set and initial model weights, and then experiment with different training regimes.
  7. Model Scoring
    • Image
    • Video

Credits