Instructions to use BAAI/seggpt-vit-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/seggpt-vit-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegGptForImageSegmentation processor = AutoImageProcessor.from_pretrained("BAAI/seggpt-vit-large") model = SegGptForImageSegmentation.from_pretrained("BAAI/seggpt-vit-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from BAAI/seggpt-vit-large: direct link, hf CLI and curl.
- Browser
- Download file 726 Bytes
-
https://huggingface.co/BAAI/seggpt-vit-large/resolve/main/config.json
- Command line
-
hf download hf://BAAI/seggpt-vit-large/config.json
-
curl -L -o config.json https://huggingface.co/BAAI/seggpt-vit-large/resolve/main/config.json
726 Bytes
| { | |
| "architectures": [ | |
| "SegGptForImageSegmentation" | |
| ], | |
| "beta": 0.01, | |
| "decoder_hidden_size": 64, | |
| "drop_path_rate": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1024, | |
| "image_size": [ | |
| 896, | |
| 448 | |
| ], | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-06, | |
| "merge_index": 2, | |
| "mlp_dim": 4096, | |
| "mlp_ratio": 4.0, | |
| "model_type": "seggpt", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "intermediate_hidden_state_indices": [ | |
| 5, | |
| 11, | |
| 17, | |
| 23 | |
| ], | |
| "patch_size": 16, | |
| "pretrain_image_size": 224, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.0.dev0", | |
| "use_relative_position_embeddings": true | |
| } | |