A S M Iftekhar, Satish Kumar, R. Austin McEver, Suya You, B.S. Manjunath.
GTNet got accepted to Pattern Recognition and Tracking XXXIV at SPIE commerce+ defence Program.
This codebase only contains code for vcoco dataset.
| Method | mAP (Scenario 1) |
|---|---|
| VSGNet | 51.8 |
| ConsNet | 53.2 |
| IDN | 53.3 |
| OSGNet | 53.4 |
| Sun et al. | 55.2 |
| GTNet | 58.3 |
- Clone repository (recursively):
git clone --recursive https://github.com/UCSB-VRL/GTNet.git
cd GTNet- Please find the data,annotations,object detection results and embeddings for vcoco here. Download it, unzip and setup the path to directory by running:
python3 setup.py -d <full path to the downloaded folder>
Folder description can be found in our old work
- Setup enviroment by running (used python 3.6.9):
pip3 install -r requirements.txt
- Download the best model from here and keep it inside a folder in the repository. We assume that you put it inside soa_vcoco folder in the repository. You can change it to anything you want.
All commands need to be run from the scripts folder.
To dump results from the best model:
bash run_inference.sh
Be sure to keep the downloaded best model in soa_vcoco folder in the repository, if you put it some other places, change the bash file accordingly. After that, to get the results in the paper run:
bash run_eval_vcoco.sh
To train with 8 GPUS run:
bash run_train.sh
Please check main.py for various flags.
Please contact Iftekhar (iftekhar@ucsb.edu) for any queries.