datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
acme-home-inbox
ACME Home Inbox
Public dataset: https://huggingface.co/datasets/Mitchins/acme-home-inbox
The v0.2.0 checkpoint contains the complete synthetic dataset plus the first
canonical OCR/vision deployment bake-off. Benchmark results are an auditable
research checkpoint, not a claim that any tested routing policy is ready for
unattended household use.
ACME Home Inbox is a fully synthetic, reproducible stress test for a practical
systems question:
Does graphical evidence change the… See the full description on the dataset page: https://huggingface.co/datasets/Mitchins/acme-home-inbox.color-multi-fractal-db-1k
Dataset Card for Color Multi Fractal DB 1k
This is a pre-generated 1k classes, 1M images colored-multi-fractal-images dataset based on Improving Fractal Pre-training by Connor Anderson et al. and Multi-Fractal-Dataset by FYSignate1009.
We have changed some fractal parameters so that our ViT pretraining can converge. Modified parameters can be found on this repo.
You can pretrain vision transformers without worrying about dataset licensing for commercial use.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Mitsua/color-multi-fractal-db-1k.fasal-mitra-sft-v1
Fasal Mitra SFT Dataset v1
Fasal Mitra ("Farmer's Friend") is a supervised fine-tuning (SFT) dataset for training
multilingual crop disease advisory AI models, specifically Gemma 4 E4B via Unsloth.
Dataset Summary
This dataset enables a vision-language model to diagnose crop diseases from photos and provide
structured agricultural advisory in Assamese, Hindi, Bengali, and English — the primary
languages of farmers in Northeast India (Assam, Meghalaya, Nagaland).… See the full description on the dataset page: https://huggingface.co/datasets/phoenix28/fasal-mitra-sft-v1.Crop_Disease_Images
Crop Disease Expert Annotations
1,092 crop images annotated by agricultural experts with ground truth diagnoses covering pests, diseases, and nutrient deficiencies across 74 crop types.
Dataset
File: annotations.csv (4 columns)
Column
Description
image_url
URL to the crop image
crop
Crop or plant identified by the expert
diagnosis
Pest, disease, or nutrient deficiency name(s); Healthy if none
details
Expert's observations on visible symptoms… See the full description on the dataset page: https://huggingface.co/datasets/Mithun2009/Crop_Disease_Images.