Computer Vision: Semantic Segmentation
It is necessary to add code for training the model
(functions train_one_epoch and evaluate, also pay attention to NOTE and TODO in different parts of the code),
the task should be performed in Google Colab/Kaggle.
Jupyter Notebook with the task attached.
Requirements:
- send a link to Google Colab/Kaggle with the solution (with saved cell outputs);
- add code for training the model for semantic segmentation;
- try different architectures for segmentation (no more than 4, and no less than 2) and choose the best one;
- tune the parameters of the optimizer/lr-scheduler to speed up the model convergence to a good solution;
- the result of the work - the notebook should contain the entire model training pipeline with comments and highlight the best model
Notes:
- Model architectures may take a long time to train (consider this to not use up all the GPU quota).
- The code may fail with a CUDA OUT OF MEMORY ERROR - reduce the batch size or the size of the image fed into the model.
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