datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GenRef-wds
GenRef-1M
We provide 1M high-quality triplets of the form (flawed image, high-quality image, reflection) collected across
multiple domains using our scalable pipeline from [1]. We used this dataset to train our reflection tuning model.
To know the details of the dataset creation pipeline, please refer to Section 3.2 of [1].
Project Page: https://diffusion-cot.github.io/reflection2perfection
Dataset loading
We provide the dataset in the webdataset format for fast… See the full description on the dataset page: https://huggingface.co/datasets/diffusion-cot/GenRef-wds.GenRef-CoT
GenRef-CoT
We provide 227K high-quality CoT reflections which were used to train our Qwen-based reflection generation model in ReflectionFlow [1]. To
know the details of the dataset creation pipeline, please refer to Section 3.2 of [1].
Dataset loading
We provide the dataset in the webdataset format for fast dataloading and streaming. We recommend downloading
the repository locally for faster I/O:
from huggingface_hub import snapshot_download
local_dir =… See the full description on the dataset page: https://huggingface.co/datasets/diffusion-cot/GenRef-CoT.xl-genimagenet-gen-sd1.5
ImageNet Generated using Stable Diffusion v1.5
The following repository mimics the size and class structure of the original ImageNet database. The classes can be found in the classes.txt file.
This dataset contains approximately 1300 images per class over 1000 classes for a total of 1.3 million images.
Here is an excerpt from classes.txt:
0 tench, Tinca tinca
1 goldfish, Carassius auratus
2 great white shark, white shark, man-eater, man-eating shark, Carcharodon caharias
3 tiger… See the full description on the dataset page: https://huggingface.co/datasets/ek826/imagenet-gen-sd1.5.Gen-nuScenesCRONOS-benchmark
CRONOS-Benchmark dataset
CRONOS-Benchmark is a controlled, synthetic benchmark for evaluating counterfactual physical consistency in video world models. It tests whether generative video models correctly simulate three fundamental physical event types — object falling, object collision, and object occlusion — across diverse scenes, objects, and viewpoints.
Each sequence provides ground-truth RGB frames, depth maps, and segmentation masks, along with a 5-frame conditioning… See the full description on the dataset page: https://huggingface.co/datasets/genintel/CRONOS-benchmark.w2w-celeba-generatedGenview_syntheric_dataset_in1klicense: apache-2.0task_categories:
feature-extractionlanguage:
enpretty_name: genview_imagenet_datasetsize_categories:
1K<n<10K
Dataset Card for GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning (ECCV 2024)
Dataset Description
The GenView ImageNet Dataset is designed for self-supervised learning, focusing on enhancing view quality through adaptive view generation. It uses the pretrained CLIP ViT-H/14 backbone to create… See the full description on the dataset page: https://huggingface.co/datasets/Xiaojie0903/Genview_syntheric_dataset_in1k.controllable-shadow-generation-benchmark
Overview
This is the public synthetic test set for controllable shadow generation created by Jasper Research Team. The project page for the research introduced this dataset is available at this link.
We created this dataset using Blender. It has 3 tracks: softness control, horizontal direction control and vertical direction control.
Example renders from the dataset below:
Softness control:
Horizontal direction control:
Vertical direction… See the full description on the dataset page: https://huggingface.co/datasets/jasperai/controllable-shadow-generation-benchmark.HY-GenDicFace-test_datasetgenimage-midjourney-10kUnified-VideoDA-Generated-FlowsOptical flows associated with our work "We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline"
See the github for full instructions, but to install run
git lfs install
git clone https://huggingface.co/datasets/hoffman-lab/Unified-VideoDA-Generated-Flows
nuscenes_generated_rangeE31_decoder_gen_fsqflux_gen_imagesgenerated_imagesE31_render_gen_vqself-generated-cyrillic-ocrflux_gen_images