In this assignment, you will implement two different object detection systems. The goals of this assignment are:
- Learn about a typical object detection pipeline: understand the training data format, modeling, and evaluation.
- Understand how to build two prominent detector designs: one-stage anchor-free detectors, and two-stage anchor-based detectors.
This assignment is due on Tuesday, March 29th at 11:59pm ET.
Q1: One-Stage Detector
The notebook one_stage_detector.ipynb will walk you through the implementation of FCOS: Fully-Convolutional One-Stage Object Detector (Tian et al, CVPR 2019). You will train and evaluate your detector on the PASCAL VOC 2007 object detection dataset.
Q2: Two-Stage Detector
The notebook two_stage_detector.ipynb will walk you through the implementation of a two-stage object detector similar to Faster R-CNN (Ren et al, NeurIPS 2015). This will combine a fully-convolutional Region Proposal Network (RPN) and a second-stage recognition network.
1. Download the zipped assignment file
2. Unzip all and open the Colab file from the Drive
Once you unzip the downloaded content, please upload the folder to your Google Drive.
Then, open each
*.ipynb notebook file with Google Colab by right-clicking the *.ipynb file.
We recommend editing your
*.py file on Google Colab, set the ipython notebook and the code side by side.
For more information on using Colab, please see our Colab tutorial.
3. Work on the assignment
Work through the notebook, executing cells and writing code in
*.py, as indicated.
You can save your work, both
*.py, in Google Drive (click “File” -> “Save”) and resume later if you don’t want to complete it all at once.
While working on the assignment, keep the following in mind:
- The notebook and the python file have clearly marked blocks where you are expected to write code. Do not write or modify any code outside of these blocks.
- Do not add or delete cells from the notebook. You may add new cells to perform scratch computations, but you should delete them before submitting your work.
- IMPORTANT: Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that has been run.
4. Evaluate your implementation on Autograder
Once you want to evaluate your implementation,
please submit the
*.ipynb and other required files to Autograder for grading your implementations in the middle or after implementing everything. You can partially grade some of the files in the middle, but please make sure that this also reduces the daily submission quota. Please check our Autograder tutorial for details.
5. Download .zip file
Once you have completed a notebook, download the completed
uniqueid_umid_A4.zip file, which is generated from your last cell of the
two_stage_detector.ipynb file. Before executing the last cell in
two_stage_detector.ipynb, please manually run all the cells of notebook and save your results so that the zip file includes all updates.
Make sure your downloaded zip file includes your most up-to-date edits; the zip file should include:
6. Submit your python and ipython notebook files to Autograder
When you are done, please upload your work to Autograder (UMich enrolled students only). Your
*.ipynb files SHOULD include all the outputs. Please check your outputs up to date before submitting yours to Autograder.