sfoucher / DOTS-summer-school-2022

Animaloc

Installation

Install the dependencies

pip install -r requirements.txt

Install the code

python setup.py install

Create a Weights & Biases account and then log in

wandb login

Usage

Starting a training session

A training session can easily be launched using the train.py tool. This tool uses Hydra framework. You simply need to modify the basic config file and then run:

python tools/train.py

You can also create your own config file. Save it first into the configs/train folder and then run:

python tools/train.py train=<your config name>

You can also make multiple different configurations runs or modify some parameters directly from the command line (see the doc).

Starting a testing session

A testing session can easily be launched using the test.py tool. This tool uses Hydra framework again. You simply need to modify the basic config file and then run:

python tools/test.py

You can also create your own config file. Save it first into the configs/test folder and then run:

python tools/test.py test=<your config name>

Visualize ground truth (and detections)

You can view your ground truth and your model's detections by using the view.py tool. This tool uses FiftyOne. You simply need to specify a root directory that contains your images (root), your CSV file containing the ground truth (gt) and optionaly a CSV file containing model's detections (-dets). See dataset format below for your CSV files format.

python tools/view.py root gt [-dets]

Dataset format

The csv_file and root_dir datasets parameters of the config file should point to a CSV file, containing your annotations, and the folder containing your images respectively.

The CSV file must contain the header images,x,y,labels (points) or images,x_min,y_min,x_max,y_max,y,labels (bounding boxes) and each row should represent one annotation, with at least, the image name (images), the object location within the image (x, y) for points, and (x_min, y_min, x_max, y_max) for bounding boxes and its label (labels):

Points dataset:

images,x,y,labels
Example.JPG,517,1653,2
Example.JPG,800,1253,1
Example.JPG,78,33,3
Example_2.JPG,896,742,1
...

Bounding boxes dataset:

images,x_min,y_min,x_max,y_max,labels
Example.JPG,530,1458,585,1750,4
Example.JPG,95,1321,152,1403,2
Example.JPG,895,478,992,658,1
Example_2.JPG,47,253,65,369,1
...

An image containing n objects is therefore spread over n lines.

Note that the names of the images in the CSV file must obviously match those in the folder pointed to by the root-dir parameter.