datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Zebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.textlatent_zebra_thinkmorph_armAB
Text-Latent (Arm A) vs All-Latent (Arm B) — Zebra-CoT + ThinkMorph
35638 samples/arm, 18 categories. Schema = ULVR/williamium style (sample_id, category, source_dataset,
question, answer, input_image, intermediate_image_N, num_intermediate_steps, messages_json).
armA_text_latent: real decoded text CoT + latent visual blocks (intermediate_image_1..3).
armB_render_latent: reasoning text RENDERED to images, all-latent baseline (intermediate_image_1..17).
messages_json = full Monet… See the full description on the dataset page: https://huggingface.co/datasets/RuoliuYang/textlatent_zebra_thinkmorph_armAB.zebra-cot-mistral-small-3.2-24b-preprocessed
Zebra-CoT Preprocessed — Mistral Hackathon 2026
Preprocessed version of the Zebra-CoT dataset for fine-tuning Mistral-Small-3.2-24B-Instruct.
Format
text: formatted as [INST] question [/INST] <think> reasoning </think> answer
image: PIL JPEG image for the corresponding visual task
Usage
Fine-tuning Mistral-Small-3.2-24B on chain-of-thought visual reasoning.
Hackathon
Created for Mistral Hackaton 2026 — Fine-tuning track with W&B.
Zebra-CoT-unify-stylezebra-herds-aerial
Dataset Description
This dataset presents labelled top-view (nadir) aerial images of plains zebra (Equus quagga) herds. The footage was captured with a DJI Mini-series drone flying at an altitude of 60 metres, in January 2025 during a field campaign at the Ol Pejeta Conservancy, Laikipia County,
Kenya. Individual video frames were extracted and every visible zebra was annotated with a bounding box, making the dataset suitable for training and evaluating object-detection models… See the full description on the dataset page: https://huggingface.co/datasets/edouard-rolland/zebra-herds-aerial.imav-2024-zebras-dataset
IMAV 2024 Zebras Detection Dataset
Object detection dataset for IMAV 2024 zebra-pattern target detection. Single class: 'Zebras'.
Dataset Structure
Split
Images
train
176
validation
59
test
58
Total images: 293
Classes: zebras
Annotation format: COCO bbox [x_min, y_min, width, height].
Usage
Load with HuggingFace Datasets
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/blackbeedrones/imav-2024-zebras-dataset.zebra-giraffe-imbalancedZebrafish-AChE-Orientation-Classificationzebra-giraffe-9000-swappedsynthetic-zebra-giraffe-02imnet1k_zebrazebra-giraffe-9000-02coco-zebra-giraffes-onlysynthetic-zebra-giraffesynthetic-zebra-giraffe-03