license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: Date
dtype: string
- name: Drone
dtype: string
- name: Timestamp_start
dtype: string
- name: Timestamp_end
dtype: string
- name: Frame
dtype: string
- name: Image
dtype: image
- name: Annotation_json
dtype: string
splits:
- name: train
num_bytes: 4720274306
num_examples: 2000
download_size: 4745590616
dataset_size: 4720274306
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Original dataset: https://zenodo.org/records/7426506
ORD for the Sciences Hackathon - Vehicles Detection
This project is an example of a hackathon project. The quality of the data produced has not been evaluated. Its goal is to provide an example on how a dataset can be update to Hugginface.
This is an example of a hackathon project presented to ORD for the sciences hackathon using the openly available pNeuma vision dataset.
Description
The goal of this project is to create a training dataset derived from the publicly available pNeuma Vision dataset, which contains drone footage and coordinates of vehicles. By leveraging machine learning techniques, specifically the "Segment Anything" model by Meta, we will accurately segment and mask the pixels corresponding to each vehicle within the footage. The resulting dataset, stored in the efficient Parquet format, will be shared on Hugging Face as a new, open-access resource for the research community. Additionally, we will document our methodology in a detailed Jupyter notebook, which will be hosted in a public GitHub repository. Our work will be registered as a derived contribution in the pNeuma RDI Hub prototype, ensuring proper attribution and fostering further research and development.
Datasets created: