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https://github.com/alaksana96/darknet-crowdhuman

YOLO Detector for the CrowdHuman Dataset. Detects people and heads. Contains training instructions on how to convert between CrowdHuman and Darknet annotations
https://github.com/alaksana96/darknet-crowdhuman

crowdhuman darknet yolov3

Last synced: 3 months ago
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YOLO Detector for the CrowdHuman Dataset. Detects people and heads. Contains training instructions on how to convert between CrowdHuman and Darknet annotations

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README 

        
# Darknet CrowdHuman



As part of my Final Year Project, I have trained the Yolo object detector on the Crowdhuman dataset. The goal was to be able to achieve fast detections on people and faces in crowds.



### NOTE:

I've realized about a year later that this repo now comes up as one of the top links on a google search for "Crowdhuman", which is nice to see. However, I keep getting emails asking for the weights I trained.



I no longer have access to the weights, they were trained and saved on the Imperial College EEE network drives. I am no longer a student at Imperial College (having graduated thanks to this project), and as such, no longer have access to the network.



TLDR; The weights are lost in the depths of the EEE network oceans.



I've left this repository up as a GUIDE to train the network. Its not too hard, and didnt take too long, about 24 hours on a 4GB GTX 1050Ti



## Darknet



![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png)



Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.



For more information see the [Darknet project website](http://pjreddie.com/darknet).



For questions or issues please use the [Google Group](https://groups.google.com/forum/#!forum/darknet).



## CrowdHuman Dataset



From the CrowdHuman [website](http://www.crowdhuman.org/):



>CrowdHuman is a benchmark dataset to better evaluate detectors in crowd scenarios. The CrowdHuman dataset is large, rich-annotated and contains high diversity. CrowdHuman contains 15000, 4370 and 5000 images for training, validation, and testing, respectively. There are a total of 470K human instances from train and validation subsets and 23 persons per image, with various kinds of occlusions in the dataset. Each human instance is annotated with a head bounding-box, human visible-region bounding-box and human full-body bounding-box. We hope our dataset will serve as a solid baseline and help promote future research in human detection tasks.



### Train/Validation

The CrowdHuman dataset can be downloaded from the [here](http://www.crowdhuman.org/download.html).



The training set is divided into 3 files, and are between 2-3GB zipped.

* ``CrowdHuman_train01.zip``

* ``CrowdHuman_train02.zip``

* ``CrowdHuman_train03.zip``



A validation set is also provided in ``CrowdHuman_val.zip``



Both the __training__ and __validation__ sets come with annotations.



* ``annotation_train.odgt``

* ``annotation_val.odgt``



#### Annotation Format

The annotations come in the ``.odtg`` format. Each line in the files is a JSON containing the annotations found in the referred image.



### Test

The test set is provided ``CrowdHuman_test.zip``. As far as I can tell, there are no labels for the test set.



## Training Darknet

Training on Darknet is never fun. There are about a million different tutorials on how to setup the files, what to do, what files to change. Many tutorials are out of date. I'm about to contribute to this mess. 



Note: This tutorial is not meant to be general 'how to'. It is how __I__ managed to train YOLO.



I trained the ``yolov3-tiny`` with ``2 classes`` on:

* Ubuntu 16.04 with Standard Darknet Setup (GPU, OPENCV, CUDNN)

* GTX 1050 Ti (4GB RAM)



### Setting up files

1. Download the 3 training zip files ``CrowdHuman_train0*.zip`` from the CrowdHuman website.

2. Extract all the files into the folder provided ``/darknet/crowdhuman_train``. You should have about __15000__ files in the folder.

3. Download the validation zip file ``CrowdHuman_val.zip``

4. Extract the validation set into ``/darknet/crowdhuman_val``. I think theres about __4370__ files in there.

5. Download the both ``annotation_train.odgt`` and ``annotation_val.odgt`` and place them in the main ``/darknet/`` folder.



### Crowdhuman to Darknet Format

CrowdHuman provides its annotations in the ``.odtg`` JSON format. Darknet does not like this. Darknet expects its annotations as such:



* Each image has a corresponding textfile containing the annotations.

    * Example: ``dog.jpg`` would have annotations in ``dog.txt``, in the same folder.



* Annotations in Darknet look like this:

    * ``    ``

    * Each line in the textfile is of that format, and each line represents an object.

    * ``x, y`` is the centre of the bounding box.

    * ``width/height`` is from the centre of the box.

    * All these values need to be __scaled__ with respect to the size of the image.

        * ``x = xCentre / imgWidth``

        * ``y = yCentre / imgHeight``

        * ``width = widthBoundingBox / imgWidth``

        * ``height = heightBoundingBox / imgHeight``

    * All the values should be between 0 and 1.



#### Generate Annotations

I have written some files which convert and create the textfiles containing the annotations:

* ``crowdhuman_train_anno.py``

* ``crowdhuman_val_anno.py``



Simply run those two files from the terminal ``python crowdhuman_*_anno.py`` from this folder, and it will generate all the corresponding textfiles in the ``/darknet/crowhuman_train`` and ``/darknet/crowhuman_val`` directories.



#### Generate Image Filepaths

Darknet also needs another textfile which contains the paths to all the training and validation images. I have written some scripts to do this:



* ``generate_train_txt.py``

* ``generate_val_txt.py``



This generates two files ``train.txt`` and ``val.txt`` in the main ``/darknet/`` directory.



### Actually Training Darknet



Just run the command:



```

./darknet detector train cfg/yolo_crowdhuman.data cfg/yolov3-tiny-crowdhuman.cfg darknet53.conv.74

```



Where ``darknet53.conv.74`` is the initial weights which one can get from:



```

wget https://pjreddie.com/media/files/darknet53.conv.74

```



#### Training Tips & Tricks

* No GUI to save GPU Memory

    * Might run out of memory on your GPU, so a good hack is to just run the training without any GUI. I used the virtual terminals ``tty1``. (Or pressing CTRL + ALT + F1)

    * I killed the GUI by running ``sudo service lightdm stop``. This left me with just a terminal and I trained the network there.



## Results

View Results:

```

 ./darknet detector test cfg/yolo_crowdhuman.data  cfg/yolov3-tiny-crowdhuman.cfg backup/yolov3-tiny-crowdhuman_30000.weights Image

 ```