Fall 2020 CS498DL
Assignment 3 Part 2: YOLO Object Detection on PASCAL VOC
Due date: Tuesday, November 3rd, 11:59:59PMCreated by Daniel McKee and Maghav Kumar. Updated by Aiyu Cui and Jeffrey Zhang.
Part 2 Task
In this part of the assignment you will implement a YOLO-like object detector on the PASCAL VOC 2007 dataset to produce results like in the above image. The goal is to help you understand the fundamentals of training an object detector, gain experience with PyTorch as well as teaching how to use pretrained models provided by the deep learning community.
How to start
Download the starting code here.
The top-level notebook (
We also provide
As you start this part, you will realize that this is a more computationally intensive assignment than what you are used to. We will soon be providing some initial expectations of mAP values as a function of epoch so you can get an early idea whether your implementation works without waiting a long time for training to converge.
You will need a GPU for this assignment, hence you should use the provided Google Cloud credits.
Environment Setup (Local)
If you will be working on the assignment on a local machine then you will need a python environment set up with the appropriate packages. We suggest that you use Conda to manage python package dependencies (https://conda.io/docs/user-guide/getting-started.html).
Unless you have a machine with a GPU, running this assignment on your local machine will be very slow and is not recommended. Please use Google Cloud for this assignment.
Be careful using GOOGLE CLOUD!! Do not use all your credits! We will soon post on Piazza how long the training is expected to take on the Cloud, but initial estimates tell us a fully trained model should take around 7-8 hours.
Data Setup (Local)Once you have downloaded the zip file, go to the Assignment3 folder and execute the download_data script provided:
The assignment is given to you in the
The instructions in the
The following resources are useful for understanding YOLO in detail:
This part of the assignment is due on Compass on due date specified above. One partner must upload the following files for this part (the netid below should be that of the submitting partner).
Please refer to course policies on collaborations, late submission, and extension requests.