Crowd Counting Project
The aim of this project is to test different methods of crowd couting. We will find an implementation of the neural network and losses developed by the articles:
- [1] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes, CVPR 2018. link
- [2] Bayesian Loss for Crowd Count Estimation with Point Supervision. link
- [3] What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? link
Data
These different methods have been tested on the ShanghaïTech Part A dataset. We provide the well formatted data for this project here. data folder has to be place in the root's project.
Project structure
The project is structured as following:
.
├── loaders
| └── bayes_loader.py # Dataset and loader of the bayes method
| └── gt_laoder.py # Dataset and loader of the ground truth method
├── models
| └── csrnet.py # csrnet nn
| └── vgg19_extended.py # vgg19 with an extension
├── losses
| └── aleatoric.py # aleatoric loss implementation
| └── bayes_loss.py
| └── port_prob.py # posterior probability of the bayes method
├── trainers
| └── abctrainer.py # abstract class defining a trainer
| └── bayes_trainer.py
| └── gt_trainer.py
├── utils
| └── loaders.py # helper functions for the loaders
| └── models.py # helper functions for the models
| └── parser.py
├── train.py # pipelines for training
├── test.y # pipelines for testing
Launching
Train exemple :
python train.py --method bayes --model csrnet --aleatoric true --lr 1e-6
Details can be found in train.py
Test exemple :
python test.py --method bayes --model csrnet
Details can be found in test.py
Output
For each training session the weights of the best models (minimizing 2 * val_mse + val_mae) are stored in the best_model_weight folder. Everything else is printed.
Tensorboard
In order the visualize metrics and results in tensorboard you need to launch it separately: tensorboard --logdir=runs. You can then access tensorboard in our browser at localhost:6006
If you have performed multiple experiments, tensorboard will aggregate them in the same dashboard.
Project made by Thomas Le Monnier de Gouville, Simon Lembeye, Robin Michard and Victor Rambaud.