| language: | |
| - en | |
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': train | |
| - name: text | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 796865621.92 | |
| num_examples: 1030 | |
| download_size: 792357206 | |
| dataset_size: 796865621.92 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| # Stable Diffusion Dataset For 3D images generation | |
| This is a set of 1.030 pairs prompt-image filtered and extracted from the 3D dataset [Objaverse](https://objaverse.allenai.org/) | |
| This Dataset was used to finetune [Stable Diffusion 2](https://huggingface.co/stabilityai/stable-diffusion-2) in order to generate good (isolated, full object ...) images to feed an image to 3D model after that (like [Triposr](https://github.com/VAST-AI-Research/TripoSR) or [CRM](https://huggingface.co/Zhengyi/CRM)). | |
| The issue we faced here was to chose which image to keep as most Objaverse objects had no title, no description or an inadequate one (for instance : School Project n°45). Thus the images have been sorted manually and by keeping around 20% of them we managed to build a 1000 big image dataset. | |