Feature Extraction
Transformers
Safetensors
sam_vision_model
remote-sensing
computer-vision
vision-transformer
sam
building-extraction
change-detection
foundation-model
Instructions to use BiliSakura/RSBuilding-ViT-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/RSBuilding-ViT-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BiliSakura/RSBuilding-ViT-B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("BiliSakura/RSBuilding-ViT-B") model = AutoModelForMultimodalLM.from_pretrained("BiliSakura/RSBuilding-ViT-B") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SamVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dtype": "float32", | |
| "global_attn_indexes": [ | |
| 2, | |
| 5, | |
| 8, | |
| 11 | |
| ], | |
| "hidden_act": "gelu", | |
| "hidden_size": 768, | |
| "image_size": 512, | |
| "initializer_range": 1e-10, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_dim": 3072, | |
| "mlp_ratio": 4.0, | |
| "model_type": "sam_vision_model", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "num_pos_feats": 128, | |
| "output_channels": 256, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "transformers_version": "5.0.0.dev0", | |
| "use_abs_pos": true, | |
| "use_rel_pos": true, | |
| "window_size": 7 | |
| } | |