datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
open-vision-banana-snvc-train-full
SNVC-50M v5_full — Multi-Task Vision Dataset
Description
This dataset is a curated subset of the SenseNova Vision Corpus 50M (SNVC-50M), containing 43,509 samples across 6 vision task families and 31 source datasets. Each sample follows a conversational format with interleaved <image> tokens, designed for training vision-language models (VLMs).
Coverage: 43,509 / 57,878 (75.2%) of the original sampling plan. 23 datasets at 100%, 8 partial, 12 unrecoverable… See the full description on the dataset page: https://huggingface.co/datasets/gatilin/open-vision-banana-snvc-train-full.bananamark-dataset
🍌 Bananamark - Who has the best Bananas?
Google released a model called Nano Banana. We had to know: is it actually good at generating bananas?
Turns out, not as good as FLUX 2 or Seedream 4!
What we did
We tested 11 image generation models on various banana-themed prompts and collected over 20'000 human preferences using the Rapidata Python API. Annotators were asked a simple question:
Which image has the better bananas?
Want to evaluate your own models? Check out… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/bananamark-dataset.BananaImageBD_variety_classification
BananaImageBD Variety Classification
A dataset for variety classification of bananas. The dataset contains raw and augmented versions.The raw dataset contains 2,471 images.Images per class:
Bangla Kola: 444
Champa Kola: 994
Sabri Kola: 509
Sagor Kola: 524
The augmented dataset contains 7,413 images.Images per class:
Bangla Kola: 1,332
Champa Kola: 2,982
Sabri Kola: 1,527
Sagor Kola: 1,572
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/BananaImageBD_variety_classification.banana_variety_classification
Banana Variety Classification
A dataset for variety classification of bananas. The dataset contains raw and augmented versions.The raw dataset contains 1,166 images.Images per class:
Anaji: 209
Bichi: 182
Champa: 136
Deshi: 237
Shagor: 239
Shobri: 163
The augmented dataset contains 6,000 images.Images per class:
Anaji: 1,000
Bichi: 1,000
Champa: 1,000
Deshi: 1,000
Shagor: 1,000
Shobri: 1,000
This dataset is indexed on https://project-agml.github.io/ as part of the AgML… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_variety_classification.banana_disease_classification_tanzania
Banana Disease Classification Tanzania
A dataset for classification of banana leaf diseases. The dataset contains 16,092 images across 3 classes: black_sigatoka, fusarium_wilt, healthy.Images per class:
black_sigatoka: 5,767
fusarium_wilt: 4,697
healthy: 5,628
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{mduma2023dataset,
title={Dataset of banana leaves and stem images for object detection… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_disease_classification_tanzania.banana_bunch_maturity_classification
Banana Bunch Maturity Classification
A dataset for mautrity classification of banana bunches. The dataset contains 2,685 images across 2 classes: Cut, Keep.
Images per class:
Cut: 1,143
Keep: 1,542
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{baglat2025multi,
title={A multi-stage dataset for banana bunch detection and harvesting decision support},
author={Baglat, Preety and Mendon{\c{c}}a… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_bunch_maturity_classification.Musa_banana_tier_classification
Musa Banana Tier Classification
This dataset contains real RGB images of banana tiers (post-harvest segments of Musa acuminata bunches) captured in a controlled laboratory environment across locations in the Philippines. Images were acquired using a fixed-position A4Tech PK-910H camera, providing standardized visual data for tier classification research in agricultural settings. The dataset contains 1,164 images across 4 classes: 1, 2, 3, 4.Images per class:
1: 390
2: 294
3:… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/Musa_banana_tier_classification.banana_leaf_disease_classification
Banana Leaf Disease Classification
A dataset for disease classification of Banana Leaves. The dataset contains 1,288 images across 3 classes: healthy, segatoka, xamthomonas.Images per class:
healthy: 154
segatoka: 320
xamthomonas: 814
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{genet2024sigatoka,
title={Sigatoka and xanthomonas banana leaf disease detection via transfer learning}… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_leaf_disease_classification.PFSD_Musa_banana_disease_classification
PFSD Musa Banana Disease Classification
A dataset for disease classification of bananas. The dataset contains 6,700 images across 8 classes: BACTERIAL SOFT ROT, BANANA APHIDS, BANANA FRUIT- SCARRING BEETLE, BLACK SIGATOKA, PANAMA DISEASE, POTASSIUM DEFICIENCY, PSEUDOSTEM WEEVIL, YELLOW SIGATOKA.Images per class:
BACTERIAL SOFT ROT: 1,078
BANANA APHIDS: 366
BANANA FRUIT- SCARRING BEETLE: 150
BLACK SIGATOKA: 474
PANAMA DISEASE: 102
POTASSIUM DEFICIENCY: 1,530
PSEUDOSTEM WEEVIL: 2… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/PFSD_Musa_banana_disease_classification.BananaLSD_leaf_disease_classification
BananaLDS Leaf Disease Classification
A dataset for disease classification of banana leaves. The dataset contains raw and augmented versions.The raw dataset contains 937 images.Images per class:
cordana: 162
healthy: 129
pestalotiopsis: 173
sigatoka: 473
The augmented dataset contains 1,600 images.Images per class:
cordana: 400
healthy: 400
pestalotiopsis: 400
sigatoka: 400
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/BananaLSD_leaf_disease_classification.banana_leaf_nutrient_classification
Banana Leaf Nutrient Classification
A dataset for classification of Banana leaf nutrient deficiencies. The dataset contains raw and augmented versions.The raw dataset contains 5,348 images.Images per class:
Boron: 173
Calcium: 794
Healthy: 1,584
Iron: 151
Magnesium: 288
Manganese: 24
Potassium: 381
Sulphur: 1,240
Zinc: 713
The augmented dataset contains 12,747 images.Images per class:
Boron: 1,384
Calcium: 1,588
Healthy: 1,586
Iron: 1,359
Magnesium: 1,440
Manganese: 1,200… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_leaf_nutrient_classification.banana_guava_quality_classification
Banana Guava Quality Classification
A dataset for quality classification of bananas and guavas. The dataset contains 1,748 images across 3 classes: Class_A, Class_B, Defect.Images per class:
Class_A: 671
Class_B: 469
Defect: 608
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{kumari2024banana,
title={Banana and Guava dataset for machine learning and deep learning-based quality classification}… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_guava_quality_classification.BananaImageBD_ripeness_classification
BananaImageBD Ripeness Classification
A dataset for ripeness classification of bananas. The dataset contains raw and augmented versions.The raw dataset contains 820 images.Images per class:
Green: 212
Overripe: 203
Ripe: 201
Semi-ripe: 204
The augmented dataset contains 2,457 images.Images per class:
Green: 636
Overripe: 609
Ripe: 603
Semi-ripe: 609
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/BananaImageBD_ripeness_classification.banana_grade_variety_classification
Banana Grade Variety Classification
A dataset for image classification of Banana Grade Variety Classification. The dataset contains 31,678 images across 3 classes: EGS109, EG2S125, EG3S113.Images per class:
EGS109: 27
EG2S125: 55
EG3S113: 46
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{guru2025banana,
title={Banana bunch image and video dataset for variety classification and grading}… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/banana_grade_variety_classification.PFSD_Musa_banana_variety_classification
PFSD Musa Banana Variety Classification
A dataset for variety classification of banana stems and leaves. The dataset contains 2,763 images across 7 classes: BHIMKOL, JAHAJI FRUIT, JAHAJI LEAF, JAHAJI STEM, KACHKOL FRUIT, MALBHOG FRUIT, MALBHOG LEAF.Images per class:
BHIMKOL: 402
JAHAJI FRUIT: 42
JAHAJI LEAF: 336
JAHAJI STEM: 204
KACHKOL FRUIT: 30
MALBHOG FRUIT: 144
MALBHOG LEAF: 1,605
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/PFSD_Musa_banana_variety_classification.