In this assignment you will implement a YOLO-like object detector on the PASCAL VOC dataset. You will mainly focus on implementing the yolo loss function and experiment with different base architectures and hyperparameters. Starter kit is provided to help you guide though the steps.

PS : Refer to lecture slides for the exact loss function (YOLO V1) to implement. Additional reference material is provided below.

1. Bonus Assignment: This assignment is not compulsory and it's mainly for those who want to improve their grade. That said, I HIGHLY encourage everyone to do it. I myself learnt a lot from it.

2. Group Assignment: This can be done in groups of at most 3. All members of a group will be graded equally.

3. Two stage submission : As mentioned in class, submission would be done in two stages. After the end of the first stage (Stage 1, due 11:59PM, May 4), you need to submit your best performing MAP scores. We will release the scores of the top three performers after which you will have another week (Stage 2, due 11:59PM, May 11) to improve your model performance. Both stages will have individual scores.

Important Note: You are only allowed to use your late days for stage 1 and NOT for stage 2. Only students who submit the scores by the end of stage 1 will be eligible for additional bonus for top-N (i.e., if you are using late days for Stage 1, your submission will be counted in top-N).

Once you have cloned this folder, execute the download_data script provided:

./download_data.sh

You will be using the train+val split (voc2007.txt) as the training dataset and the test dataset (voc2007test.txt) as your validation or local test dataset. The actual test dataset would be released shortly.

Instructions are provided in the yolo_loss.py file to help you guide through this assignment. Once you have implemented the loss function, feel free to change other parts of the network to boost the performance of your model.

First and foremost, install all required packages by running the command:

pip install -r requirements.txt

Once installed and you have implemented the loss function, you can train using the command:

python main.py --name="exp_name" --B=2 --S=14 --learning-rate=0.001 --num-epochs=50 --batch-size=24 --lambda-coord=5 --lambda-noobj=0.5

where

This will create a model file <exp_name>_best_detector.pth which will store weights of the model. Tensorboard logging is also integrated, so while training you can check your loss plots through tensorboard by running the command on a different terminal window:

tensorboard --logdir=tb_log/ --bind_all

Go to the link that appears to visualise your plots.

Once trained, you can evaluate the performance by running the command:

python main.py --eval --model-path="/path/to/model"

where

This will create a my_solution.csv which will contain MAP scores for each of the class.

Once you have implemented the loss and successfully finish one iteration of training, feel free to change other parts of the network to boost the performance of your model.

For each of the stages you need to upload three things on ELMS:

1. A ZIP of all code files including model file.
2. my_solution.csv - This needs to be uploaded separately.
3. A brief report on the changes you made to boost performance.This needs to be uploaded separately.

Please folow all the instructions, failure to do so will result in deduction of points.

Due dates:

1. Stage 1 - 11:59PM, May 4 (May the force be with you)
2. Stage 2 - 11:59PM, May 11

Here are some additional reporces that might help for the assignment:

1. Slide 41 of Lecture slides.
2. Original YOLO Paper.
3. Great explanation of the Yolo Loss function.

I will be holding a single combined office hours on May 4 to clear all doubts. I will have a poll on piazza for the exact timing.

The assignment is inspired from Assignment 3 Part 2 of CS498.