Simple object detection

This work illustrates how to build an object detection algorithm prototype from image labelling to qualitative and quantitative evaluation of a model. All you need to get started is a set of raw images that are relevant to the task you want to address.

This workflow was presented and run during LabelFlow's Webinar on how to build and AI model in no time

The inspiration for this work comes from the torchvision object detection tutorial with the minimum requirements to actually use the trained model added on top. The framework is based on PyTorch and most of the utils are just copied from torchvision references. The framework's inputs and outputs are all datasets in COCO format, which is one of the reference formats for manipulating annotated images.

Data labellisation and visualization are done on LabelFlow an open-source annotation platform that doesn't need any sign-up and that doesn't store your images.

0 - Install the requirements

• Make sure that you have python 3.x installed. This was tested with python 3.8.0.

• It is recommended to create a new virtual environment to avoid interferences with your global libraries, you can follow this tutorial to create one. Additionaly, run pip install Cython on your blank virtual environment before installing the requirements.

• If you wish to make use of a GPU, you should make sure that the version of torch you use is compatible with your environment. Just follow the instructions on PyTorch.

pip install -r requirements.txt

You should then be able to run the following line without encountering any issue.

python train.py --dataset-path data/sample-coco-dataset

The device that is used for training is logged at the beginning of the training script, e.g. "Running training on device cpu" or "Running training on device cuda". This can help you make sure that your GPU is actually being used if you wish to use it.

1 - Label your images and train a model

A viable dataset should have the following properties:

• Big enough: there's between 100 and 1000 labels per class. Keep in mind that the higher this number is, the better the model will get.

• Balanced: there's approximately the same amount of labels per class

• Consistent: the images' distribution reflects the reality of the task you're trying to address

Connect on LabelFlow, upload and label your raw images. Export them to COCO format, making sure that you toggle the options to include the images.

The following script will train a new model for you on the coco dataset that you just exported - or any coco format dataset respecting the structure of data/sample-coco-dataset.

python train.py --dataset-path <coco-dataset-directory-path>

After each epoch, the script will print evaluation metrics on a validation dataset that is split from the original one. The model's snapshot weights will be stored after each training epoch in outputs/models/<dataset name>/epoch_<snapshot index>.pth.

2 - Make inferences and visualize them

The following script runs your model on a coco dataset and generates a coco annotation file containing the inferences in outputs/inferences/<dataset name>_annotations.json. Typically, you can create a raw dataset on LabelFlow and export it as shown above to generate the correct input for the model.

python detect.py --dataset-path <coco-dataset-directory-path> --model-path <model-snapshot-path>

You can then qualitatively evaluate your prototype by importing the output annotation file to the corresponding raw dataset on LabelFlow as shown in the above schema.

3 - Next steps

You can tune the parameters in train.py and in detect.py to optimize the performance of your prototype. If the results are satisfying, you can add more training data and switch to a more scalable and configurable framework like Detectron2.