datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-one-temp-1_1-RL-a-keasyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-one-temp-1_1-RLeasyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-stage-three-temp-1_7-RL-zero-correct-to-0.2easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-two-temp-1_1-RLeasyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-stage-two-temp-1_1-RL-zero-correct-to-0.3ground-truth-mmmu-pro-vision-sampling-500easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-stage-two-temp-1_1-RL-zero-correct-to-0.2temp_sampling_sftThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "panda",
"total_episodes": 10,
"total_frames": 640,
"total_tasks": 9,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 10,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/red0orange/temp_sampling_sft.ground-truth-mmmu-pro-standard-10-sampling-500latent_diffusion_super_sampling
Dataset Card for Latent Diffusion Super Sampling
Image datasets for building image/video upscaling networks.
This repository contains implementation of training and inference code for models trained on the following works:
Part 1: Trained Sub-Pixel Convolutional Network for Upscaling on 5000 individual 720p-4K and 1080p-4K image pairs
References: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, Shi et al… See the full description on the dataset page: https://huggingface.co/datasets/aoxo/latent_diffusion_super_sampling.sampling-for-dream-boothSampling_Mixture_50k_v2