Usage

• Inference
• Evaluation
• Training

Inference

Prerequisites

It is expected that yolov5 is installed on the same level as the repos root dir (see illustration below).
If you'd like to install or already have it installed elsewhere, see Optional Arguments in the next section.

├── hidden-object-recognition-with-yolov5
│   ├── datasets
│   ├── model
│   ├── src
│   ...
├── yolov5
│   └── ...

Infer

First, open a terminal inside the repos root directory, then run

python src/inference/predict.py input_path

With input_path being a path to a single image or a directory of images on which to infer on.

Optional Arguments

• --output_path Specifies a directory in which the prediction images with visible bounding boxes will be saved. It won't be used if not specified.
• --yolov5_path Use this to point to yolo's root dir.
  
• --weights_path Use this to change the weights which will be used for inference.
• --team_name Defaults to 'Tieeeeeeeeeeem'.

Results

The Inference will produce a .csv file for each image inside the input_path containing labels in yolov5 format. See below for a short description.
The file will be named after image_name.team_name.csv. For example, when inference is made on an image x.png, the resulting .csv file will be called x.Tieeeeeeeeeeem.csv since team_name defaults to 'Tieeeeeeeeeeem'.

Additionally, if --output_path is specified, a directory containing images with visible bounding boxes will be created at the specified path.

CSV Format

Parameter	Description
Object	Class ID
x	Center x-position of the label. Normalized to image width.
y	Center y-position of the label. Normalized to image height.
w	Normalized label width.
h	Normalized label height.

Evaluation

Prerequisites

For evaluation, you'll need Prof. Hänig's Wimmelbild Generator ().

Evaluate

Open a terminal inside the Wimmelbild Generator root directory and run

python src/wimmelbild_generator/evaluation_cli.py input_path Tieeeeeeeeeeem

With input_path being the path to the same directory used when inferring.

That's it.

Training

Prerequisites

For this workflow to function as intended, you'll have to have Docker, the NVIDIA Container Toolkit, Nvidia Drivers installed, and you'll need a yolov5 Docker image. Instructions for all this can be found here.

These steps will suffice to give you a working yolov5 image, but to fully use our Augmentation Pipeline, you'll need a modified version of this image, which needs some manual manipulation. If you'd like to, you can:

Build the image yourself

After completing the above steps, we need to enable an augmentation library called albumentations. This is achieved by running the previously downloaded image in interactive mode, so we can alter some files.

1) Start the Docker container and make some changes

To start the container, type

docker run --ipc=host -it --gpus all ultralytics/yolov5:latest  

You should be inside the docker container now. Now you have to manipulate some files:

• First, use any text editor to uncomment line 38 inside the requirements.txt.
  • Save and close the file afterwards.
• Then use the same editor to remove lines 24-31 from utils/augmentations.py.
• Now paste the following in its place and save and close the file afterwards.

T = [
  A.Blur(p=0.4, blur_limit=(3, 20)),
  A.ISONoise(p=0.4, intensity=(0.5, 2.0))]

Exit the container with

exit

2) Create an image from the altered container

Run

docker ps -a

and look for your container. Use the IMAGE column to identify it.
With the container ID, use the following step to create the image.

docker commit [YOUR_CONTAINER_ID] ultralytics/yolov5:albumentations

Train

Having a terminal open inside the repos root dir, type

src/train/yolo_docker.sh

which will automatically download the full training set from Roboflow, create a validation set from it and start the yolov5 Docker container to start training on 2 GPUs.
The script yolo_docker.sh is by far not flexible. It's intended for use on our own training machine only!

When the training is finished, you'll find the results in runs/train/.

Training Parameters Used

Parameter	Value
Epochs	300
Batch Size	16 (8 per GPU)
Image Size	640
Pretrained Weights	yolov5x.pt
Hyperparameter	hyp.HiddenObject.yaml + Blur & ISO (Albumentations)