datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ViT-FineTuneFUSION-Finetune-12M
FUSION-12M Dataset
Please see paper & website for more information:
https://arxiv.org/abs/2504.09925
https://github.com/starriver030515/FUSION
Overview
FUSION-12M is a large-scale, diverse multimodal instruction-tuning dataset used to train FUSION-3B and FUSION-8B models. It builds upon Cambrian-1 by significantly expanding both the quantity and variety of data, particularly in areas such as OCR, mathematical reasoning, and synthetic high-quality Q&A data. The goal is… See the full description on the dataset page: https://huggingface.co/datasets/starriver030515/FUSION-Finetune-12M.llava-finetunelibero-icl-finetune-plus-90OpenNiji-Dataset-Aesthetic-Finetune-0-15K
Dataset Card for "OpenNiji-Dataset-Aesthetic-Finetune-0-15K"
More Information needed
crophelth_finetune_2nd_stage
CropHelth Round-2 Fine-Tune Dataset
Author: Hansaka Rasanjana
Project Context: First-year IoT group mini-project at the Sri Lanka Institute of Information Technology (SLIIT)
5,457 brand-new leaf images across 60 of the 112 crop-disease classes — zero overlap with the original 134,016-image crophelth training set. Put together to fine-tune our round-1 model (train_report.json: accuracy 0.9484, macro F1 0.8889), with per-class sampling weighted heavily toward our weak classes.… See the full description on the dataset page: https://huggingface.co/datasets/hansaka01/crophelth_finetune_2nd_stage.libero-icl-finetune-4.0.0This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 858,
"total_frames": 138289,
"total_tasks": 20,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:858"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ryanhoangt/libero-icl-finetune-4.0.0.dlcv_final_data_finetune
DLCV Final Dataset
This dataset is used for the Deep Learning for Computer Vision (DLCV) final project.It contains ground-truth layers organized per sample and is designed for training and evaluating computer vision models.
📂 Dataset Structure
The dataset is organized as follows:
dlcv_final/
├── gt_layers/
│ ├── sample_0000/
│ │ ├── layer_0.png
│ │ ├── layer_1.png
│ │ └── ...
│ ├── sample_0001/
│ ├── sample_0002/
│ └── ...
└── README.md
Each sample_xxxx directory… See the full description on the dataset page: https://huggingface.co/datasets/dereklin1205/dlcv_final_data_finetune.receipts-finetune-v2receipts-finetune-v3re10kDl3dv_mixMode_1Sub2D_TrueAlpha_finetune_0.95MixWeight_unionMask_TrueDetach3Dreceipts-finetune-v1gemini-finetune-imagesLLava-sharegpt4v-finetune-ocr_vqa-231206pi_finetune_20250923_195721This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "widowx_ai",
"total_episodes": 0,
"total_frames": 0,
"total_tasks": 0,
"total_videos": 0,
"total_chunks": 0,
"chunks_size": 1000,
"fps": 10,
"splits": {},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kobikelemen/pi_finetune_20250923_195721.ScenicOrNot_224_fine-tune_rev2
Dataset Card for ScenicOrNot 224 Fine-Tune (Rev 2)
Dataset Summary
This dataset is a preprocessed, fine-tuning-ready version of the ScenicOrNot dataset, designed for training machine learning models on landscape aesthetics. The images in this version have been standardized to a 224x224 resolution, making them directly compatible with standard vision models (such as ResNet, MobileNet, and ViT) that expect a 1:1 aspect ratio.
The dataset includes the corresponding… See the full description on the dataset page: https://huggingface.co/datasets/ddecosmo/ScenicOrNot_224_fine-tune_rev2.plots_llama_3.1_8b_finetuned_binaryGemma4-FinetuneKit
[!NOTE]
⚠️ Note: Use Pytorch template 2.4. This works best on 12B because it is specifically calibrated for A100 SXM on runpod. Download v2 for multiarch support.
💎 Gemma 4 Finetune Kit
by @Naphula
Here is the complete, start-to-finish guide using .tar.gz and your exact Hugging Face repository (26B-Suite/Gemma4-FinetuneKit).
This was developed for use with Runpod A100 SXM but can be adapted to other server types.
You should adjust settings like learning rate, epoch etc. as… See the full description on the dataset page: https://huggingface.co/datasets/26B-Suite/Gemma4-FinetuneKit.exp-Nayana-IR-DescVQA-finetune-hi-10kGemma4-FinetuneKit-v2
[!WARNING]
⚠️ Note: Not fully functional yet, still in progress.
[!NOTE]
⚠️ Note: Use Pytorch template 2.4. This version has multiarch + python 3.11/3.12 support.
💎 Gemma 4 Finetune Kit v2
by @Naphula
Version 2 adds MultiArch support for easier finetuning of 26B/31B
You can literally launch up an H200 SXM, copy in your trainRunpod script, and launch this oneshot command to finetune.
export HF_TOKEN="hf_InsertTokenHere"
pip install hf && \
hf download… See the full description on the dataset page: https://huggingface.co/datasets/26B-Suite/Gemma4-FinetuneKit-v2.exp-Nayana-IR-DescVQA-finetune-test_train_hi_10kSa2VA-finetune-examplepi_finetune_20250920_115916This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "widowx_ai",
"total_episodes": 10,
"total_frames": 9271,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kobikelemen/pi_finetune_20250920_115916.geoguessr-countries-finetune
GeoGuessr Countries Finetune
Google Earth images from around the world with the country as the target label.
Splits
Split
Samples
train
25000
test
400
Columns
image: Google Earth image
country: country label
Countries In This Release
Argentina, Australia, Austria, Bangladesh, Belgium, Bolivia, Botswana, Brazil, Bulgaria,
Cambodia, Canada, Chile, Colombia, Croatia, Czechia, Denmark, Finland, France, Germany,
Ghana, Greece, Hungary… See the full description on the dataset page: https://huggingface.co/datasets/moondream/geoguessr-countries-finetune.SAM_finetune_dot_traning_datasetpi05_lm3_lora_finetuneChatTime-1-Finetune-100K
ChatTime: A Multimodal Time Series Foundation Model
✨ Introduction
In this paper, we innovatively model time series as a foreign language and construct ChatTime, a unified framework for time series and text processing. As an out-of-the-box multimodal time series foundation model, ChatTime provides zero-shot forecasting capability and supports bimodal input/output for both time series and text. We design a series of experiments to verify the superior performance of… See the full description on the dataset page: https://huggingface.co/datasets/ChengsenWang/ChatTime-1-Finetune-100K.SAM_finetune_dataset_dot_training1finetunevqarecod-finetune