A deep neural net Libraby Built on top of Pytorch
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Modles - Contains the dnn models
- NewResnetModel.py - Dawnbench mark 2019 winners model
- QuizModel.py - Dense Net
- ResNetModel.py - Resnet
- S7Model.py - custom model
- MaskDepthModel - model that estimates both mask and depth
- DepthModel - Model that estimates depth
- MaskModel - Model that estimates mask
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Dataset - contains data related modules
- extract.py - Unzips the data set for monocular depth estimation and segmentation
- MaskDepth.py - It brings the depth estimation andsegmentation to dataset format and applies the given transformations.
- tinyimagenet - It downloads the tiny imagenet data, mix train-test, split into the given ratio and returns train and test set of type dataset.
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Evaluation Metrics
- Accuracy.py - Implements the dice score for evaluation of mask and depth.
- loss.py - Implementation of different loss functions.
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Results
- showMnD - displays the predicted and target images of mask and depth.
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Training
- train_test_MnD.py - training for depth estimation and segmentation.
- train_test.py - training for object recognisation.
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Albumentation transforms - Used for Image Agumentations. It is from Albumentations library.
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GradCam - Implements gradCam of the given images and specified layer of the model.
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LrFinder - It finds the Lr of given range.
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LR_Range_test - It finds the best Lr for One Cyce Policy
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evaluate - It evaluates the final test accuracy, classwise accuracy, plots the given curves, gives misclassified inages and plots misclassified images,
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show_images - Plots the given images of tensor for. Mainly used to visualise the train data.
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train_test - Used to train the model.
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train_test_loader - takes the train test data of type dataset, converts into data loader form, set the seed, check for the cuda availability.