datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SoyNet_leaf_health_classification
SoyNet Leaf Health Classification
A dataset for health classification of soy leaves. The dataset contains 3,655 images across 2 classes: Disease, Healthy.Images per class:
Disease: 3,210
Healthy: 445
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{rajput2023soynet,
title={SoyNet: A high-resolution Indian soybean image dataset for leaf disease classification},
author={Rajput, Arpan Singh and… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/SoyNet_leaf_health_classification.plant-disease-crop-health-15classes
🌿 Plant Leaf Disease Dataset (15 Classes - Unsegmented RAW)
📌 Dataset Summary
This dataset contains high-quality RGB images of healthy and diseased plant leaves across 3 major agricultural crops: Pepper (Bell), Potato, and Tomato. The dataset is structured into 15 distinct classes covering viral, bacterial, fungal diseases, pest infestations, and healthy controls.
Unlike segmented datasets, this collection consists of raw (unsegmented) RGB images, providing a… See the full description on the dataset page: https://huggingface.co/datasets/Ramseyn-77/plant-disease-crop-health-15classes.kenya-bee-health-qa-image-triples
Kenya Bee Health Training Data
This folder is a starter database for BeeCare Anywhere / Gemma Apiary. It is intentionally small, transparent, and license-aware: use it to prove the Q/A/image-triple pipeline, then expand it with Kenyan field data before trusting model behavior in production.
Important Model Note
google/gemma-2b is a text-to-text, decoder-only model. It cannot directly read pictures. Use these image triples with a vision-capable model path, for… See the full description on the dataset page: https://huggingface.co/datasets/yahelr1/kenya-bee-health-qa-image-triples.synthetic_wildlife_health
Synthetic Wildlife Health: Camera Trap Imagery for Alopecia and Body Condition Screening
Dataset Summary
This dataset contains 553 synthetic camera trap images depicting alopecia (hair loss consistent with mange) and body condition deterioration in North American wildlife, along with paired visual question-answering annotations for health assessment tasks.
All images are AI-generated edits of real camera trap photographs sourced from iWildCam 2022. The generative pipeline… See the full description on the dataset page: https://huggingface.co/datasets/BrundageLab/synthetic_wildlife_health.Leaf-Health-Status-Classification-Dataset
Leaf Health Status Classification Dataset
The current agricultural industry faces challenges in pest and disease monitoring and management, especially in large-scale plantations, where manual inspection is costly and inefficient. The application of existing machine vision technology in target detection and classification is not yet widespread, leading to an inability to promptly respond to pest invasions. This dataset aims to provide high-quality leaf health status classification… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Leaf-Health-Status-Classification-Dataset.Healthcare-Imaging-DatasetDataset Description:
This dataset is a large-scale collection of healthcare images, containing 341,527 images, designed to support the development and training of advanced computer vision, medical imaging, healthcare AI, and multimodal AI systems.
Additionally, this dataset can be used in pipelines for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows, improving model performance in medical image understanding, disease recognition, anatomical… See the full description on the dataset page: https://huggingface.co/datasets/InfoBayAI/Healthcare-Imaging-Dataset.