Fall 2020 CS498DL
Assignment 3 Part 1: Multi-label Image Classification
Due date: Tuesday, November 3rd, 11:59:59PMCreated by Daniel McKee and Maghav Kumar. Updated by Aiyu Cui and Jeffrey Zhang.
Source: PASCAL VOC website
This assignment have multiple parts. START EARLY!!!
In this part of the assignment you will implement a multi-label image classifier on the PASCAL VOC 2007 dataset. You will design and train deep convolutional networks to predict a binary present/absent image-level label for each of the 20 PASCAL classes. This will help you gain experience with PyTorch. You will:
How to start
Download the starting code here.
The top-level notebooks (
You will be required to use a GPU for this assignment, and hence you will be given Google Cloud credits shortly. However, we encourage you to use Colaboratory to get started and for debugging purposes, as you have limited number of credit hours for this and the following assignments.
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.
Data Setup (Local)
Once you have downloaded the zip file, go to the Assignment3 folder and execute the download_data script provided:
Data Setup (For Colaboratory)
If you are using Google Colaboratory for this assignment you will need do some additional setup steps.
You will need to run the
You will now need to open the assignment 3 ipython notebook file from your Google Drive folder in Colaboratory and run a few setup commands. Make sure to set the GPU as the hardware accelerator. To do this, on the top bar choose Edit->Notebook Settings-> select 'GPU'. We have condensed all the important setup commands you need to run into an ipython notebook here.
The assignment is given to you in the
As before, submission will be done on Compass by one of the partners. The following items must be uploaded for Part 1 (the netid below should be that of the submitting partner).
Please refer to course policies on collaborations, late submission, and extension requests.