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Release v0.1-beta

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@animikhaich animikhaich released this 06 Apr 18:55
· 31 commits to main since this release
v0.1-beta
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Initial Release with Basic Features

  • Dockerfile
  • Launch Script (Dependency: GNU Parallel)
  • Automatic Tensorboard Initialization (Using the Launch Script) on Port 6006
  • Frontend:
    • Streamlit Dashboard For Easy Training and Visualization
    • Epoch Count and Batch Progress Bar with Training Status Message
    • Live Training & Validation Loss & Accuracy Plots on the dashboard using Plot.ly Graphs
    • Training and Validation Data Directory
    • Model Backbone Selector
    • Training Optimizer Selector
    • Learning Rate Slider
    • Batch Size Slider
    • Max Number of Epochs Selector
    • Input Image Shape Selector
    • Training Precision Selector
    • Training Button
    • Status Update with Final Validation Accuracy and Balloons Animation on Completion
  • Data Loader:
    • Optimized Tf.Data implementation for maximum GPU usage
    • Automatically handle errors such as corrupted images
    • Built-in Dataset Verification
    • Built-in Checks for if dataset is of a supported format
    • Supports Auto Detect Sub-folders get class information
    • Auto Generate Class Label Map
    • Built in Image Augmentation
    • Dataset Batch Visualization (With and Without Augment)
  • Model Trainer:
    • Support for Multiple Model Selection (All the models available to Keras)
    • Support for Loading Pre-Trained Model and Resume Training
    • Support for Mixed Precision Training for both GPUs and TPU optimized workloads
    • Support for Keras to Tensorflow SavedModel Converter
    • Contains a method to run Inference on a batch of input images
    • Dynamic Callbacks:
      • Automatic Learning Rate Decay based on validation accuracy
      • Automatic Training Stopping based on validation accuracy
      • Tensorboard Logging for Metrics
      • Autosave Best Model Weights at every epoch if validation accuracy increases
      • Support for any custom callbacks in addition to the above
    • Available Metrics (Training & Validation):
      • Categorical Accuracy
      • False Positives
      • False Negatives
      • Precision
      • Recall
      • Support for any custom metrics in addition to the above
  • Supported Models:
    • MobileNetV2
    • ResNet50V2
    • Xception
    • InceptionV3
    • VGG16
    • VGG19
    • ResNet50
    • ResNet101
    • ResNet152
    • ResNet101V2
    • ResNet152V2
    • InceptionResNetV2
    • DenseNet121
    • DenseNet169
    • DenseNet201
    • NASNetMobile
    • NASNetLarge
    • MobileNet
  • Supported Optimizers:
    • SGD
    • RMSprop
    • Adam
    • Adadelta
    • Adagrad
    • Adamax
    • Nadam
    • FTRL
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