aditi184/Person_Re-Identification

Person ReIdentification using Locally Aware Transformers

Person Re-Identification

Results

On validation set

Model	CMC@rank-1	CMC@rank-5	mAP	Download
Baseline	92.9	96.4	91.5	model
LA-TF++ (ours)	92.9	1.0	93.2	model

Installation

pip install -r requirements.txt

Running Models

Training

Locally-Aware Transformer (Baseline)

python train_baseline.py --train_data_dir ./data/train --model_name la-tf_baseline --model_dir ./model

LA-TF++ (Our model)

python run-train.py --train_data_dir ./data/train --model_name la-tf++_final --model_dir ./model

Testing

python run-test.py --model_path <path-to-saved-model> --test_data ./data/val

The script run-test.py takes in the query and gallery images (present in the test_data) and computes the following metrics:

1. CMC@rank-1
2. CMC@rank-5
3. mean Average Precision (mAP)

Visualization

python run-test.py --model_path <path-to-saved-model> --test_data ./data/val --visualize --save_preds <path-to-save-images>

Dataset

The dataset has 114 unique persons. The train and val set contain 62 and 12 persons, respectively. Each person has been captured using 2 cameras from 8 different angles.

Acknowledgements

• Locally Aware Transformer (LA-TF) is adaped from Person Re-Identification with a Locally Aware Transformer.
• Triplet Loss and Label Smoothing are adapted from Alignedreid++.

Authors

• Shubham Mittal
• Aditi Khandelwal

Computer Vision course project (course webpage) taken by Prof. Chetan Arora