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A Multi-Task Learning Approach to Person Re-Identification and Attribute Recognition

A multi-task learning approach to person re-identification and attribute recognition. Written in PyTorch, trained and evaluated on the Market-1501 dataset. Check the report for more implementation details.

Model architecture

 

Results

Attribute Recognition AccuracyRe-identification mAP
attribute ResNet ConvHead Self-Attention
age 78.63 83.09 74.50
backpack 81.83 82.86 75.98
bag 73.97 70.08 75.36
handbag 90.47 90.19 90.38
clothes 89.47 92.10 92.36
down 89.26 87.08 85.86
up 93.94 93.62 93.10
hair 84.10 84.03 84.12
hat 96.35 96.47 96.51
gender 87.80 89.37 82.14
uppercolor 69.96 69.25 65.74
lowercolor 66.14 64.30 62.89
average 83.49 83.53 81.58
ResNet ConvHead Self-Attention
67.86 69.93 62.93

Get started

This project was developed using Google Colab, to execute it:

  • Create a Deep Learning folder in the Google Drive root. Alternatively, change the GDRIVE_HOME_PATH constant in the notebook to use a custom root path.
  • Upload the .ipynb notebook to Google Drive.
  • Upload the dataset.zip file to Google Drive into Deep Learning/dataset/dataset.zip. Alternatively, change the GDRIVE_DATASET_PATH constant in the notebook to use a custom dataset path.
  • Run the notebook.

Note: different hyper-parameter combinations can be tested by changing the parameter values of the function main, called in the "Run" section.

Code structure

The notebook is organized in different sections:

  • Imports: external libraries imports.
  • Dataset: definition of the Dataset and BatchSampler classes and a utility to load and transform the data.
  • Model definition: implementation of the three models, each with its separate class.
  • Loss functions: definition of the loss classes: ReIDLoss, AttributeRecognitionLoss and MultiTaskLoss.
  • Accuracy functions: implementation of the accuracy functions used to evaluate the results for attribute recognition and re-identification.
  • Train and test: the main logic for training and validating the models.
  • Run: instantiation of dataset, model, optimizer and loss functions. The hyper-parameters can be configured here.
  • Final predictions: construct the final results for the test and query sets.