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https://github.com/peterampazzo/dtu-02456


https://github.com/peterampazzo/dtu-02456

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README 

        
# 02456 Deep Learning Project



**TRANSFER LEARNING: PASSENGERS AND HELMET USE ON MOTORCYCLES IN MYANMAR AND NEPAL**



Mobility can differ among different places. Researchers have studied how people move in Myanmar and Nepal, focusing on the usage of motorcycles. Much effort has been put into identifying, in an automated way, motorcycles from video footage using deep learning techniques and classifying the position of riders and helmets. This project has applied transfer learning from a labelled traffic dataset from Myanmar to classify the position encoding of passengers and helmets on motorcycles to a dataset from Nepal, with the aim of utilising the datasets similarities to construct a model for the latter one. The results are promising, showing the model trained on Myanmar has performed even better on the Nepal dataset.



## Project Structure 



```

├── app.conf                           <- Shared settings across the repo

├── README.md                          <- The top-level README for developers using this project

├── notebooks                          <- All notebooks

|   ├── inspect_model.csv              <- Inspect the achitecture of the save model 

|   ├── train_val_acc_loss_plots.ipynb 

│   └── within_class_graphs.ipynb

├── src

|   ├── bike_detection.py            <- Bike detection using YOLOv3

│   ├── classification_encode.py     <- Classification Encoding

│   ├── data_split.py                <- Data Splitting

│   ├── merge_annotations.py         <- Merge annotation files

│   └── utils.py                     <- Utility functions

├── scripts

|   ├── cp_nepal.sh                   <- Copy Nepal data set from shared folder

|   ├── dl_myanmar.sh                 <- Download Myanmar data set from the web

│   └── run_on_hpc.sh                 <- Run classification_encode.py on HPC with GPU

│

└── requirements.txt                  <- The requirements file for reproducing the analysis environment

```



## Development



Find below all the instructions to set up your enviroment, run Jupyter Lab and use DTU HPC with GPU.



### How to set up local env:



```

virtualenv env

source env/bin/activate # MacOS

.\env\Scripts\activate # Windows

pip install --upgrade pip

pip -r requirements.txt

```



### How to run Jupyter locally:

```

# cd into project folder

source env/bin/activate # MacOS

.\env\Scripts\activate # Windows



jupyter lab

```



### How to run bike detection script:

```

git clone https://github.com/eriklindernoren/PyTorch-YOLOv3.git pytorchyolo

bash ./pytorchyolo/weights/download_weights.sh

mv -t pytorchyolo/weights/ yolov3-tiny.weights yolov3.weights darknet53.conv.74



# Then run:

python src/bike_detections.py

```



## Run on HPC

[Guide to HPC](https://docs.google.com/document/d/1pBBmoLTj_JPWiCSFYzfHj646bb8uUCh8lMetJxnE68c/edit)



The model can be trained running `python src/classification_encode.py`.

The script requires some arguments to be executed:



```

usage: classification_encode.py [-h] [--load LOAD] [--train | --no-train] [--tuning | --no-tuning]

                                [--save SAVE]

                                project



positional arguments:

  project               Project to run.



optional arguments:

  -h, --help            show this help message and exit

  --load LOAD           (Optional) Saved model to load.

  --train, --no-train   (Optional) if model needs to be trained.

  --tuning, --no-tuning

                        (Optional) if model needs to be fine tuned.

  --save SAVE           (Optional) Filename for saving the model.

```



Be sure to adapt the bash script accordingly for the desired output.



```

# Make sure a logs file exists

mkdir logs



# Adapt the bash script according to the model you want to run, then run

bsub < ./scripts/run_on_hpc.sh 

```



## Datasets



Extra storage has been requested to DTU for this project. Since the provided data for Myanmar wasn't completed, we created our own copy. Moreover, to facilitate the preprocessing we made a copy of the Nepal dataset as well.



#### MYANMAR



```

bash ./scripts/dl_myanmar.sh

python src/merge_annotations.py Myanmar # check

python src/data_split.py Myanmar

```



#### NEPAL



```

bash ./scripts/cp_nepal.sh

python src/merge_annotations.py Nepal

python src/data_split.py Nepal

```



## Authors 



* Christina Nørgaard Bartozzi

* Erla Hrafnkelsdóttir 

* Pietro Rampazzo