Omkar2118/datasets huggingface dataset:
Cow Pose Estimation Dataset

Description:

<a href="https://gts.ai/dataset-download/cow-pose-estimation-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset has been specifically curated for cow pose estimation, designed to enhance animal behavior analysis and monitoring through computer vision techniques. The dataset is annotated with 12 keypoints on the cow’s body, enabling precise tracking of body movements and posture. It is structured in the COCO format, making it compatible with popular deep learning models like YOLOv8, OpenPose, and others designed for object detection and keypoint estimation tasks.

Applications:

This dataset is ideal for agricultural tech solutions, veterinary care, and animal behavior research. It can be used in various use cases such as health monitoring, activity tracking, and early disease detection in cattle. Accurate pose estimation can also assist in optimizing livestock management by understanding animal movement patterns and detecting anomalies in their gait or behavior.

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Keypoint Annotations:

The dataset includes the following 12 keypoints, strategically marked to represent significant anatomical features of cows:

Nose: Essential for head orientation and overall movement tracking.

Right Eye: Helps in head pose estimation.

Left Eye: Complements the right eye for accurate head direction.

Neck (side): Marks the side of the neck, key for understanding head and body coordination.

Left Front Hoof: Tracks the front left leg movement.

Right Front Hoof: Tracks the front right leg movement.

Left Back Hoof: Important for understanding rear leg motion.

Right Back Hoof: Completes the leg movement tracking for both sides.

Backbone (side): Vital for posture and overall body orientation analysis.

Tail Root: Used for tracking tail movements and posture shifts.

Backpose Center (near tail’s midpoint): Marks the midpoint of the back, crucial for body stability and movement analysis.

Stomach (center of side pose): Helps in identifying body alignment and weight distribution.

Dataset Format:

The data is structure in the COCO format, with annotations that include image coordinates for each keypoint. This format is highly suitable for integration into popular deep learning frameworks. Additionally, the dataset includes metadata like bounding boxes, image sizes, and segmentation masks to provide detail context for each cow in an image.

Compatibility:

This dataset is optimize for use with cutting-edge pose estimation models such as YOLOv8 and other keypoint detection models like DeepLabCut and HRNet, enabling efficient training and inference for cow pose tracking. It can be seamlessly integrate into existing machine learning pipelines for both real-time and post-processed analysis.

This dataset is sourced from Kaggle.
Telugu Document Image Dataset

Description:

<a href="https://gts.ai/dataset-download/telugu-document-image-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset consists of 20,000 image-text pairs designed to aid in training machine learning models capable of extracting text from scanned Telugu documents. The images in this collection resemble “scans” of documents or book pages, paired with their corresponding text sequences. This dataset aims to reduce the necessity for complex pre-processing steps, such as bounding-box creation and manual text labeling, allowing models to directly map from image inputs to textual sequences.

The main objective is to train models to handle real-world scans, particularly those from aged or damaged documents, without needing to design elaborate computer vision algorithms. The dataset focuses on minimizing the manual overhead involved in traditional document processing methods, making it a valuable resource for tasks like optical character recognition (OCR) in low-resource languages like Telugu.

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Key Features:

Wide Variety of Realistic Scans: The dataset includes images mimicking realistic variations, such as aging effects, smudges, and incomplete characters, commonly found in physical book scans or older documents.

Image-Text Pairing: Each image is linked with its corresponding pure text sequence, allowing models to learn efficient text extraction without additional manual preprocessing steps.

Customizable Data Generation: The dataset is built using open-source generator code, which provides flexibility for users to adjust hundreds of parameters. It supports custom corpora, so users can replace the probabilistically generated “gibberish” text with actual texts relevant to their use cases.

Scalable and Efficient: Thanks to parallelized processing, larger datasets can be generated rapidly. Users with powerful computational resources can expand the dataset size to 
hundreds of thousands or even millions of pairs, making it an adaptable resource for large-scale AI training.

Multi-Script Support: The generator code can easily be extended to other scripts, including different Indic languages or even non-Indic languages, by modifying the Unicode character set and adjusting parameters such as sentence structure and paragraph lengths.

Dataset Applications:

This dataset is especially useful for developing OCR systems that handle Telugu language documents. However, the data generation process is flexible enough to extend to other Indic languages and non-Indic scripts, making it a versatile resource for cross-lingual and multi-modal research in text extraction, document understanding, and AI-driven translation.

This dataset is sourced from Kaggle.