Course CS236781: Deep Learning Final Mini Project

You can find the full project assignment here.

The Guidelines as given:

In this part you'll implement a small comparative-analysis project, heavily based on the materials from the tutorials and homework.

You should implement the code which displays your results in this notebook, and add any additional code files for your implementation in the project/ directory. You can import these files here, as we do for the homeworks.

Running this notebook should not perform any training - load your results from some output files and display them here. The notebook must be runnable from start to end without errors.

You must include a detailed write-up (in the notebook) of what you implemented and how.

Explain the structure of your code and how to run it to reproduce your results.

Explicitly state any external code you used, including built-in pytorch models and code from the course tutorials/homework.

Analyze your numerical results, explaining why you got these results (not just specifying the results).

Where relevant, place all results in a table or display them using a graph.

The Projects Goals

You need to perform Object Detection task, over 7 of the dataset.

The annotation for object detection can be downloaded from here: https://github.com/wimlds-trojmiasto/detect-waste/tree/main/annotations.

The data and annotation format is like the COCOAPI: https://github.com/cocodataset/cocoapi (you can find a notebook of how to perform evalutation using it here: https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb) (you need to install it..)

if you need a beginner guild for OD in COCOAPI, you can read and watch this link: https://www.neuralception.com/cocodatasetapi/