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DLCV Final Project ( Visual Queries 2D Localization Task )

How to run your code?

  • TODO: Please provide the scripts for TAs to reproduce your results, including training and inference. For example,
bash train.sh <Path to clips folder> <Path to annot file>
bash inference.sh <Path to clips folder> <Path to annot file> <Path to output json file>

Usage

To start working on this final project, you should clone this repository into your local machine by the following command:

git clone https://github.com/ntudlcv/DLCV-Fall-2023-Final-2-<team name>.git

Note that you should replace <team_name> with your own team name.

For more details, please click this link to view the slides of Final Project - Visual Queries 2D Localization Task. The introduction video for final project can be accessed in the slides.

Visualization

We provide the code for visualizing your predicted bounding box on each frame. You can run the code by the following command:

python3 visualize_annotations.py --annot-path <annot-path> --clips-root <clips-root> --vis-save-root <vis-save-root>

Note that you should replace <annot-path>, <clips-root>, and <vis-save-root> with your annotation file (e.g. vq_val.json), the folder contains all clips, and the output folder of the visualization results, respectively.

Evaluation

We also provide the evaluation for you to check the performance (stAP) on validation set. You can run the code by the following command:

cd evaluation/
python3 evaluate_vq.py --gt-file <gt-file> --pred-file <pred-file>

Note that you should replace <gt-file> with your val annotation file (e.g. vq_val.json) and replace <pred-file> with your output prediction file (e.g. pred.json)

Submission Rules

Deadline

112/12/28 (Thur.) 23:59 (GMT+8)

Q&A

If you have any problems related to Final Project, you may

  • Use TA hours
  • Contact TAs by e-mail ([email protected])
  • Post your question under [Final challenge 2] VQ2D discussion section in NTU Cool Discussion