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Structure of the code:

Code Related to Yolo:

  1. backbone.py: This file contains the code to build Resnet Backbone for Yolo.
  2. createValDataset.py: This file contains the code to Split train.csv into train and val sets.
  3. dataset.py: Contains the code to pack the data into pytorch Dataset to make it suitable for dataloader.
  4. loss.py: Contains the code for loss function of yolo.
  5. model.py: Contains the code to build YoloV1 model.
  6. train.py: Contains the code to train the model.
  7. utils.py: Contains all the helper functions used in the codebase.

Instructions to execute:

  • Create a python environment and install pyTorch and other required libraries.
  • Obtain the Voc dataset from Kaggle.
  • run Python createValDataset.py in terminal
  • run python train.py to train and get metrics of model.

Code Related to Detr:

  1. main.py: Heart of the system which binds all the things together.
  2. model1.py: Builds the Detr model.
  3. utils1.py: Contains all the helper functions for the system.
  4. evaluate.py: Contains the code for evaluating the model.
  5. run python main.py to to perform visualizations of results and get metrics and pass 1 and the true_boxes as cmd arguements.

Acknowlegements:

  • Youtube of Alladion Person and Yannic Kilcher were very helpful in doing our project.

Link to PPT: https://drive.google.com/drive/folders/1wZVRThEGT-Pw4T9fpPMm74m9XFDJQUsc?usp=drive_link Link to presentation: https://drive.google.com/drive/folders/1wZVRThEGT-Pw4T9fpPMm74m9XFDJQUsc?usp=drive_link

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  • Python 92.4%
  • Jupyter Notebook 7.6%