Omkar2118/datasets huggingface dataset:
Ref-AVS

Ref-AVS seeks to segment objects within the visual domain based on expressions containing multimodal cues.
Dahlias Flower Variety Dataset

Description:

<a href="https://gts.ai/dataset-download/dahlias-flower-variety-dataset/" target="_blank">👉 Download the dataset here</a>

The Dahlias Flower Variety Dataset​ is an extensive compilation of high-resolution images and detailed metadata for a diverse range of dahlia flower varieties. Known for their vibrant colors and varied forms, dahlias are a popular subject among gardeners and photographers alike.

Download Dataset

Dataset Contents:

Images: This dataset includes a wide array of images featuring various dahlia flower varieties. The photos are taken under different lighting conditions, settings, and angles, providing a rich resource for researchers, florists, and hobbyists.

Metadata: Each image comes with comprehensive metadata, including the flower variety name, color, bloom size, and other pertinent details. This metadata is invaluable for research, classification, and identification purposes.

Use Cases:

Botanical Research: Researchers and botanists can utilize this dataset to study and classify dahlia flower varieties, enhancing our knowledge of the species.

Horticulture: Gardening enthusiasts and professionals can reference this dataset to identify different varieties, plan landscaping projects, and design vibrant flower arrangements.

Computer Vision and Machine Learning: This dataset is ideal for data scientists and machine learning practitioners to train and test models for flower recognition, classification, and 
segmentation.

This dataset is sourced from Kaggle.
SpaceNet Comprehensive Astronomical Dataset

Description:

<a href="https://gts.ai/dataset-download/dahlias-flower-variety-dataset/" target="_blank">👉 Download the dataset here</a>

SpaceNet is a hierarchically structured and high-quality astronomical image dataset, created using a novel double-stage augmentation process. This dataset, comprising approximately 12,900 images, is designed for both fine-grained and macro classification tasks. SpaceNet incorporates a range of resolutions from lower (LR) to higher resolution (HR) images, using standard augmentations and a diffusion approach for generating synthetic samples. This allows for superior generalization across various recognition tasks such as classification. The dataset also includes diverse celestial objects, making it a valuable resource for both academic research and practical applications in astronomy and astrophysics.

Download Dataset

Dataset Structure:

Fine-Grained Classes: The dataset includes 8 distinct classes: planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.

Dataset Composition:

Total Samples: Approximately 12,900 images

Fine-Grained Class Distribution:

Asteroid: 283 images

Black Hole: 656 images

Comet: 416 images

Constellation: 1,552 images

Galaxy: 3,984 images

Nebula: 1,192 images

Planet: 1,472 images

Star: 3,269 images

Usage: SpaceNet is ideal for:

Training and evaluating machine learning models on fine-grained and macro astronomical classification tasks.

Conducting research on hierarchical classification methods within the astronomy field.

Developing robust models that demonstrate excellent generalization across both in-domain and out-of-domain datasets.

This dataset is sourced from Kaggle.
Alpaca Dataset Image Classification

Description:

<a href="https://gts.ai/dataset-download/alpaca-dataset-image-classification/" target="_blank">👉 Download the dataset here</a>

The Alpaca Dataset is a collection of JPEG images designed for binary image classification tasks, specifically classifying images as “Alpaca” or “Not Alpaca”. This dataset is ideal for training and fine-tuning machine learning models using transfer learning techniques.

Download Dataset

Context

This small dataset is perfect for educational purposes, initial model testing, and developing proof-of-concept applications in image classification. Due to its limited size, it is most beneficial when used in conjunction with transfer learning to leverage pre-trained models for improved accuracy.

Content

The dataset is organized into two primary directories:

Alpaca: Contains images that include alpacas.

Not Alpaca: Contains images without alpacas, featuring subjects that may resemble alpacas but are not.

Additional Information

Format: All images are in JPEG format, ensuring compatibility with a wide range of image processing libraries and tools.

Usage: This dataset can be utilized in various machine learning frameworks such as TensorFlow, PyTorch, and Keras for building and testing classification models.

Applications: Potential applications include animal recognition systems, educational tools, and development of AI-driven content moderation systems.

Data Statistics

Total Images: X (Number of images in the dataset)

Alpaca Images: Y (Number of images in the Alpaca directory)

Not Alpaca Images: Z (Number of images in the Not Alpaca directory)

Image Resolution: Varies, with most images having a resolution suitable for quick model training and evaluation.

This dataset is sourced from Kaggle.
Chess Positions Dataset

Description:
 
<a href="https://gts.ai/dataset-download/chess-positions-dataset/" target="_blank">👉 Download the dataset here</a>

To build a model capable of generating FEN (Forsyth-Edwards Notation) descriptions based on schematic images of a chessboard, we need to outline a comprehensive project plan that includes several key components:

Download Dataset

Content:

Images: 100,000 images of randomly generated chess positions with 5-15 pieces (2 kings and 3-13 pawns/pieces).
Generated using 28 styles of chess boards and 32 styles of chess pieces, totaling 896 board/piece style combinations.

All images are 400×400 pixels.

Training set: 80,000 images

Test set: 20,000 images

Piece Distribution:

Pawn: 30%

Bishop: 20%

Knight: 20%

Rook: 20%

Queen: 10%

2 Kings are always present on the board.

Annotations: Each image is annotated with the corresponding FEN description to facilitate supervised learning.

Variability: The dataset includes a wide variety of board and piece styles to ensure robust model training across different visual representations.

Applications: This dataset is ideal for training computer vision models to accurately recognize and classify various chess positions. By using advanced image processing and machine learning techniques, these models can analyze board configurations, identify pieces

Tools: Images were generated using a custom-built tool, ensuring consistency and quality in the dataset.

This dataset is sourced from Kaggle.