OSDD

This project contains the Object State Detection Dataset (OSDD). The images along with the corresponding annotations can be found in the following links:

• TRAIN : https://drive.google.com/file/d/1nZK8wYqShCtZf6jjNkOeOQuXHLC41HCI/view?usp=sharing
• VALIDATION : https://drive.google.com/file/d/1v1eajK8IwcYSpg_iI8y4uhSnQUh_VmiG/view?usp=sharing
• TEST : https://drive.google.com/file/d/1AsMkY08ZEBTwKYP8TpNcBqTDWh7Op-Sx/view?usp=sharing
• DATA FILE :https://drive.google.com/file/d/1nvUEmFudYMYSvfSiabOCpuPSMAAFUSC4/view?usp=sharing

In order to test your custom images the weights and the configuration file of the network must be downloaded:

• WEIGHTS : https://drive.google.com/file/d/1KIOoVQCdBOtJJLOgL829jiN1pKBxVwTQ/view?usp=sharing
• CONFIG FILE : https://drive.google.com/file/d/1jr-YxiP4xUhxpyT43f_xrl-qmMmEjkEs/view?usp=sharing

Plase note the that the annotations follow the YoloV4 format.

Requirements

• Python >= 3.6

• A Linux-based environment

• OpenCV

• YoloV4

How to visualize the dataset

1. Download the images from the links that are presented above.
2. Change the variable images_path in the src/paths.py so that it points to the directory where the images are stored (If you have downloaded all three parts of images, i.e. train,validation and test, you have to point to the path of the part you want to visualize).
3. Run the following command in the terminal:

python src/main.py --visualize

How to test the state detector on custom images

1. Download the weights and data and configuration file from the links that are presented above.
2. Change the variables weights_path,conf_file_path and data_file_path in the src/paths.py so that they points to the directory where the previous files were stored.
3. Install the YoloV4 in your machine as explained in this link.
4. Change the variable detector_exec_path in the src/paths.py so that it points to the directory where the executable file of Yolo is located.
5. Run the following command in the terminal:

python src/main.py --test path_where_custom_images_are_found

Citation

If you use our annotations in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@inproceedings{gouidis2022, title={ Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study}, author={Gouidis, F. and Patkos, T. and Argyros, A. and Plexousakis, D. }, booktitle={ Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)}, year={2022}, volume={5}, pages={590--600} }