Change Log

v2.3b2

  • Fixed bug in Grad-CAM API that caused error when calculating forward propagation in model
  • Fixed bug in forward propagation method definition that allowed model to calculate output even when number of classes were undefined

v2.3b1

  • Added prediction scores column to prediction.csv output on “Predict” GUI page for Classification models
  • Changed Model Distribution plot to only show blinking point for model that is currently training
  • Changed Model Distribution plot to remove points for model in CREATED or RESERVED states
  • Fixed bug in classmap loading function for SSD object detection algorithm
  • Fixed bug in Deeplabv3+ segmentation algorithm classmap definition
  • Fixed bug in model state where model would not enter RESERVED state

v2.3b0

  • Added Deeplabv3+ segmentation model to GUI
  • Revised GUI component details

v2.2b1

  • Made GUI text selectable
  • Fixed bugs

v2.2b0

  • Added Deeplabv3+ segmentation model to API
  • Refactored GUI components
  • Refactored backend server
  • Refactored CNN model architecture code
  • Modified Yolov2 loss function
  • Modified Yolov1 and Yolov2 pretrained weights
  • Fixed bugs

v2.1b3

  • Fixed bugs
  • Revised documentation

v2.1b2

  • Added Grad-CAM visualization tool
  • Added pytests
  • Revised README.md format
  • Added URLs to download past wheel packages to README.md
  • Fixed bugs

v2.1b1

  • Modify img loader to accept binary image

v2.1b0

  • Add new augmentation methods
  • Add a function for downloading the prediction result as csv
  • Modify image data preprocess pipeline
  • Update Node.js and Python dependencies
  • Fix UI Bugs

v2.0.4

  • Update dependencies

v2.0.3

  • Fix UI Bugs

v2.0.2

  • Add warning to dataset create modal(GUI) when illegal train valid ratio is inputted
  • Add error handler to renom_img.api.inference.detector.Detector

v2.0.1

  • Fixed UI bugs
  • Updated webpack @ 3 . x => webpack @ 4 . x
  • Modified eslint settings
  • Modified Darknet architecture (Added BatchNormalization and removed bias term from convolution layer)
  • Modified Yolov1 loss function