datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Anime-Background-Finetuning-V1.1
Anime-Background-Finetuning (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of anime Screencap data and 8k of scrapped danbooru illustration data.
This is the proccessed version of the dataset meant to be used for my personal finetuning practice project, please visit my RicemanT/Background-Finetuning repo for the raw unprocessed data that you can process yourself.
The dataset have two minor type of processing being done… See the full description on the dataset page: https://huggingface.co/datasets/HappyHenAi/Anime-Background-Finetuning-V1.1.happy-whale-dolphin-classificationhappysugarlife
Bangumi Image Base of Happy Sugar Life
This is the image base of bangumi Happy Sugar Life, we detected 35 characters, 2876 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/happysugarlife.haotian_data-GPS-AR-Lopti-master
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
Authors: Zhihe Yang$^{1}$, Xufang Luo$^{2*}$, Zilong Wang$^2$, Dongqi Han$^2$, Zhiyuan He$^2$, Dongsheng Li$^2$, Yunjian Xu$^{1*}$,
($^*$ for corresponding authors)
The Chinese University of Hong Kong, Hong Kong SAR, China
Microsoft Research Asia, Shanghai, China
Introduction
In this study, we identify a critical yet underexplored issue in RL training: low-probability tokens disproportionately… See the full description on the dataset page: https://huggingface.co/datasets/happynew111/haotian_data-GPS-AR-Lopti-master.SafeMT
Dataset Description
This dataset comprises a total of 10,000 samples,
including 2,000 harmful queries, each paired with corresponding images,
and 8,000 dialogues categorized across 17 distinct scenarios.
They adhere strictly to the usage policy outlined by OpenAI and the policy guidelines of the Gemini App.
For each identical harmful query, we generate dialogues of varying lengths—specifically, dialogues consisting of two, four, six, and eight turns while maintaining consistency… See the full description on the dataset page: https://huggingface.co/datasets/HappyCorpse/SafeMT.MINT-Safecustom_vqa_similarityHappy
News
[2025/02/18]: We add the original captions of PubMedVision in PubMedVision_Original_Caption.json, as well as the Chinese version of PubMedVision in PubMedVision_Chinese.json.
[2024/07/01]: We add annotations for 'body_part' and 'modality' of images, utilizing the HuatuoGPT-Vision-7B model.
PubMedVision
PubMedVision is a large-scale medical VQA dataset. We extracted high-quality image-text pairs from PubMed and used GPT-4V to reformat them to enhance their quality.… See the full description on the dataset page: https://huggingface.co/datasets/Bulamalaminu001/Happy.FaceExpressions_Happy_Sad_Surprisehappy-to-sad-dataset-trainimage_dataMMLongBench_base_deepeyesautodex-gallery-proxyobject_detectionFaceExpressions_Happy_Sad_SurpriseCircuitSense
CircuitSense
This dataset is a comprehensive multimodal circuit question-answering benchmark designed to evaluate visual reasoning and problem-solving capabilities across three main domains: Perception, Analysis, and Design. The dataset contains structured question-answer pairs with accompanying visual content, targeting different engineering cognitive levels and reasoning tasks.
Dataset Structure
The dataset is organized into three primary folders, each containing… See the full description on the dataset page: https://huggingface.co/datasets/happynewyue/CircuitSense.happy-to-sad-evalMMLongBench_image_question_only_deepeyes_prompt_revisedsiglip_classification_result_bestcheckpoint_20251217_003311
SigLIP Classification Eval
Model: /hub_data3/seohyun/outputs/siglip_ecva/best
Test repo: happy8825/siglip_test (split: train)
Samples visualized: 50 of 924
Metrics
accuracy: 0.6732
f1: 0.7051
total: 924, correct: 622
Confusion Matrix
MMLongBench_image_question_only_deepeyes_concat_viz_concatsiglip_classification_result_20251217_010209
SigLIP Classification Eval
Model: happy8825/siglip-ecva-main
Test repo: happy8825/siglip_test (split: train)
Samples visualized: 50 of 924
Metrics
accuracy: 0.5790
f1: 0.5482
total: 924, correct: 535
Evaluation Runtime
dataset size: 924
total time: 23.80 sec
avg infer time (per sample): 6.54 ms
GPU memory avg: 363.5 MiB
GPU memory peak: 370.9 MiB
Confusion Matrix
mememultilabel-image-classificationgvpo_v2_r1siglip_classification_result_20251216_234307
SigLIP Classification Eval
Model: /hub_data3/seohyun/outputs/siglip_ecva
Test repo: happy8825/siglip_test (split: train)
Samples visualized: 50 of 924
Metrics
accuracy: 0.5736
f1: 0.5321
total: 924, correct: 530
Confusion Matrix
test_custom_datasetsiglip_classification_result_20251217_011529
SigLIP Classification Eval
Model: happy8825/siglip-ecva-main
Test repo: happy8825/siglip_test (split: train)
Samples visualized: 50 of 924
Metrics
accuracy: 0.5790
f1: 0.5482
total: 924, correct: 535
Evaluation Runtime
dataset size: 924
total time: 24.64 sec
avg infer time (per sample): 6.97 ms
GPU memory avg: 363.5 MiB
GPU memory peak: 370.9 MiB
Confusion Matrix
happyOrSad
Dataset Card for "happyOrSad"
More Information needed
happy_tree_friends_datasetmmlongbench_summary_debug_with_images
Usage
from datasets import load_dataset
dataset = load_dataset("happy8825/mmlongbench_summary_debug_with_images")
row = dataset["train"][0]
row["page_image"].show()