xView1 Adapted for YOLOv5 in Colab

Overview:

This dataset is a modified version of the xView1 dataset, specifically tailored for seamless integration with YOLOv5 in Google Colab. The xView1 dataset originally consists of high-resolution satellite imagery labeled for object detection tasks. In this adapted version, we have preprocessed the data and organized it to facilitate easy usage with YOLOv5, a popular deep learning framework for object detection.

Dataset Contents:

Images: The dataset includes a collection of high-resolution satellite images covering diverse geographic locations. These images have been resized and preprocessed to align with the requirements of YOLOv5, ensuring efficient training and testing.

Annotations:

Object annotations are provided for each image, specifying the bounding boxes and class labels of various objects present in the scenes. The annotations are formatted to match the YOLOv5 input specifications.

Usage Instructions:

1. Download the dataset files, including images and annotations.
2. Clone the YOLOv5 repository in Colab.
3. Move dataset files (train.txt and val.txt) to the yolov5 directory.
4. Use the provided .yaml for training.

{"references": ["Zou, DN., Zhang, SH., Mu, TJ.\u00a0et al.\u00a0A new dataset of dog breed images and a benchmark for finegrained classification.\u00a0Comp. Visual Media\u00a06,\u00a0477\u2013487 (2020). https://doi.org/10.1007/s41095-020-0184-6"]} Preprocessed dataset for Tsinghua Dogs in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes

Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).

FOOD IMAGE SEGMENTATION will build a food image segmentation model using YOLOv5 to identify and segment different food items in images. This has applications in calorie counting, dietary tracking, food waste reduction, restaurant food ordering, and automated recipe generation. The project will collect and preprocess food image data, train a YOLOv5 model, evaluate its performance, and integrate it into a web or mobile app. Expected outcomes include a robust food image segmentation model and its integration into various food-related applications.

An activity for Mapua University Data Science Specialization.

In this activity CO2-FA2 ,we are tasked to preprocess the data and create our own dataset using Roboflow for YOLOv5 and YOLOv7.

Names of the Students: Mallari, Andrei Bench Tan, John Caleb