Omkar2118/datasets huggingface dataset:
Lego Road image for Drone

Description:

<a href="https://gts.ai/dataset-download/lego-road-image-for-drone/" target="_blank">👉 Download the dataset here</a>

This folder contains three distinct types of images representing various movements: forward, left, and right. Our task involves reading these images into a digital array, which will enable us to analyze and predict the next action of the drone.

Download Dataset

Here’s a detailed breakdown of the process:

Image Categorization:

Forward Movement: These images depict scenarios where the drone needs to move straight ahead.

Left Movement: These images illustrate situations where the drone needs to turn or move to the left.

Right Movement: These images show instances where the drone should turn or move to the right.

Image Preprocessing:

Reading Images: Use an appropriate library to read and load the images from the folder into a digital format.

Resizing and Normalizing: Standardize the size of the images and normalize the pixel values to ensure consistent input for the analysis model.

Labeling: Assign labels to each image based on its movement category (forward, left, right).

Data Storage:

Store the preprocessed images in a digital array, with each image associated with its respective label. This array will serve as the dataset for further analysis.

Analysis and Prediction:

Feature Extraction: Extract relevant features from the images, such as edges, shapes, and textures, which are indicative of the movement type.

Model Training: Use machine learning algorithms to train a predictive model on the labeled dataset. This model will learn to recognize patterns corresponding to each movement type.

Prediction: Utilize the trained model to analyze new images and predict the drone’s next action based on the detected patterns.

Applications:

Real-Time Navigation: Implement the predictive model in the drone’s navigation system to enable real-time decision-making based on visual inputs.

Autonomous Operations: Enhance the drone’s autonomous capabilities by integrating the model with other sensors and control systems for seamless movement.

This dataset is sourced from Kaggle.
Long-RVOS

This work proposes Long-RVOS, a large-scale benchmark for long-term video object segmentation. Long-RVOS is the first minute-level dataset in the RVOS field, designed to tackle various realistic long-video challenges such as frequent occlusion, disappearance-reappearance, and shot changing. Notably, Long-RVOS offers significantly longer video duration than existing datasets. In addition, it contains the largest number of object classes and mask annotations. The large scale of Long-RVOS supports comprehensive training and evaluation of RVOS models. Finally, we gather 24,689 high-quality descriptions for building Long-RVOS.

Introducing paper: Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
NIAB teff phenotyping platform

Description:

<a href="https://gts.ai/dataset-download/niab-teff-phenotyping-platform/" target="_blank">👉 Download the dataset here</a>

The dataset consists of 1120 high-resolution RGB images, all taken from a top-down perspective of various pots. These images were obtained from a phenotyping platform developed at the National Institute of Agricultural Botany (NIAB) to analyze teff roots and shoots. Each pot contained 16 seeds from different mutant teff strains to study their growth in a shared environment. The seeds were arranged around the perimeter of transparent pots to allow for side-view imaging of root growth, although these specific images are not included in this dataset.

Download Dataset

The pots were divided into four groups, each containing 40 pots. Each group, or block, is labeled as EXP{X}_Block{Y}. The genotypes were duplicated in each block and placed on a bench with varying airflow gradients. Photos were taken every two to three days to monitor plant development, resulting in a series of seven images per pot. Each day’s images are stored in separate directories per block, with the first directory containing the earliest images and the final directory containing the latest images.

This dataset is sourced from Kaggle.