Instructions to use torch-pointcloud/point-bert-base.dvae.xumin-yu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- torch-pointcloud
How to use torch-pointcloud/point-bert-base.dvae.xumin-yu with torch-pointcloud:
import torch_pointcloud as tp model = tp.create_model("point-bert-base.dvae.xumin-yu", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from torch-pointcloud/point-bert-base.dvae.xumin-yu: direct link, hf CLI and curl.
- Browser
- Download file 524 Bytes
-
https://huggingface.co/torch-pointcloud/point-bert-base.dvae.xumin-yu/resolve/main/config.json
- Command line
-
hf download hf://torch-pointcloud/point-bert-base.dvae.xumin-yu/config.json
-
curl -L -o config.json https://huggingface.co/torch-pointcloud/point-bert-base.dvae.xumin-yu/resolve/main/config.json
524 Bytes
| { | |
| "library_name": "torch-pointcloud", | |
| "name": "point-bert-base.dvae.xumin-yu", | |
| "task": "pretraining", | |
| "hparams": { | |
| "in_channels": 0, | |
| "num_groups": 64, | |
| "group_size": 32, | |
| "encoder_dims": 256, | |
| "token_local_channels": [ | |
| 128, | |
| 256 | |
| ], | |
| "token_global_channels": [ | |
| 512 | |
| ], | |
| "num_tokens": 8192, | |
| "tokens_dims": 256, | |
| "decoder_dims": 256 | |
| }, | |
| "input_keys": [ | |
| "x", | |
| "pos", | |
| "batch" | |
| ], | |
| "dataset": "shapenet55", | |
| "author": "xumin-yu", | |
| "license": "MIT" | |
| } | |