datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
humanoid-pose-state-dataset-lite
Humanoid Pose State Dataset Lite
Lightweight synthetic dataset for humanoid robot pose classification.
Pose Classes
neutral
walking_pose
running_pose
sitting_pose
lifting_pose
waving_pose
Structure
dataset/
├── train/
├── validation/
Each split contains pose-labeled image folders.
Total Samples
Train: 600
Validation: 150
Image Format
RGB, 224x224
License
MIT
birdsnap_liteThis is a version of BirdSnap that will be easier on your free Google Colab quota.
TFQ-Data-Lite
TFQ-Data: A Fine-Grained Dataset for Image Implication
TFQ-Data is a large-scale visual instruction tuning dataset specifically designed to train Multi-modal Large Language Models (MLLMs) on Image Implication and Metaphorical Reasoning.
Unlike standard VQA datasets that focus on literal description, TFQ-Data utilizes a True-False Question (TFQ) format. This format provides high knowledge density and verifiable reward signals, making it an ideal substrate for Visual Reinforcement… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Data-Lite.TFQ-Bench-Lite
TFQ-Bench: A Benchmark for Evaluating Image Implication Understanding
TFQ-Bench is a rigorous evaluation benchmark designed to assess the capabilities of MLLMs in understanding visual metaphors, sarcasm, and implicit meanings via True-False Questions.
It serves as a complement to existing benchmarks like II-Bench (Multiple-Choice Question) and CII-Bench (Open-Style Question), offering a lower-bound difficulty check that tests a model's ability to verify specific propositions about… See the full description on the dataset page: https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Lite.