Upload RSBuilding-Swin-T

README.md

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+---
+license: mit
+tags:
+- remote-sensing
+- computer-vision
+- swin-transformer
+- building-extraction
+- change-detection
+- foundation-model
+datasets:
+- remote-sensing-images
+model-index:
+- name: RSBuilding-Swin-T
+  results: []
+library_name: transformers
+pipeline_tag: feature-extraction
+---
+
+# RSBuilding-Swin-T
+
+HuggingFace Transformers version of RSBuilding Swin-Tiny model, converted from MMDetection/MMSegmentation format.
+
+## Source
+
+- **Source Code**: [https://github.com/Meize0729/RSBuilding](https://github.com/Meize0729/RSBuilding)
+- **Original Checkpoint**: [https://huggingface.co/models/BiliSakura/RSBuilding](https://huggingface.co/models/BiliSakura/RSBuilding)
+
+## Model Information
+
+- **Architecture**: Swin Transformer Tiny
+- **Embedding Dimension**: 96
+- **Depths**: [2, 2, 6, 2]
+- **Number of Heads**: [3, 6, 12, 24]
+- **Window Size**: 7
+- **Image Size**: 224×224
+- **Patch Size**: 4×4
+
+## Important Notes
+
+### Missing Buffer Keys (Expected)
+
+When loading this model, you may see messages about missing buffer keys (typically ~12 keys). **This is expected and normal.**
+
+These missing keys are buffers that are computed dynamically during model initialization:
+- `relative_position_index`: Precomputed index mapping for window-based attention
+- `relative_coords_table`: Precomputed coordinate table for relative positions
+- `relative_position_bias_table`: Precomputed bias table
+
+**Why they're missing:**
+- These buffers are recalculated each time the model is instantiated based on `window_size` and other configuration parameters
+- They don't need to be saved in checkpoints because they're deterministic and computed from config
+- This is standard behavior in HuggingFace Swin transformers
+
+**Action required:** None. The model will work correctly with these buffers computed automatically.
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install transformers torch pillow
+```
+
+### Inference Example
+
+```python
+from transformers import SwinModel, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Load model and processor
+model = SwinModel.from_pretrained("BiliSakura/RSBuilding-Swin-T")
+processor = AutoImageProcessor.from_pretrained("BiliSakura/RSBuilding-Swin-T")
+
+# Load and process image
+image = Image.open("your_image.jpg")
+inputs = processor(image, return_tensors="pt")
+
+# Forward pass
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get features
+# outputs.last_hidden_state: (batch_size, num_patches, hidden_size)
+# outputs.pooler_output: (batch_size, hidden_size) - pooled representation
+features = outputs.last_hidden_state
+pooled_features = outputs.pooler_output
+
+print(f"Feature shape: {features.shape}")
+print(f"Pooled feature shape: {pooled_features.shape}")
+```
+
+### Feature Extraction for Downstream Tasks
+
+```python
+from transformers import SwinModel, AutoImageProcessor
+import torch
+
+model = SwinModel.from_pretrained("BiliSakura/RSBuilding-Swin-T")
+processor = AutoImageProcessor.from_pretrained("BiliSakura/RSBuilding-Swin-T")
+
+# Process image
+image = Image.open("your_image.jpg")
+inputs = processor(image, return_tensors="pt")
+
+# Extract features
+with torch.no_grad():
+    outputs = model(**inputs)
+    
+# Use pooled features for classification/regression
+features = outputs.pooler_output  # Shape: (1, 768)
+
+# Or use last hidden state for dense prediction tasks
+spatial_features = outputs.last_hidden_state  # Shape: (1, num_patches, 768)
+```
+
+## Model Configuration
+
+The model uses the following configuration:
+- `image_size`: 224
+- `patch_size`: 4
+- `num_channels`: 3
+- `embed_dim`: 96
+- `depths`: [2, 2, 6, 2]
+- `num_heads`: [3, 6, 12, 24]
+- `window_size`: 7
+- `mlp_ratio`: 4.0
+- `hidden_act`: "gelu"
+
+## Citation
+
+If you use this model, please cite the original RSBuilding paper:
+
+```bibtex
+@article{wangRSBuildingGeneralRemote2024a,
+  title = {{{RSBuilding}}: {{Toward General Remote Sensing Image Building Extraction}} and {{Change Detection With Foundation Model}}},
+  shorttitle = {{{RSBuilding}}},
+  author = {Wang, Mingze and Su, Lili and Yan, Cilin and Xu, Sheng and Yuan, Pengcheng and Jiang, Xiaolong and Zhang, Baochang},
+  year = {2024},
+  journal = {IEEE Transactions on Geoscience and Remote Sensing},
+  volume = {62},
+  pages = {1--17},
+  issn = {1558-0644},
+  doi = {10.1109/TGRS.2024.3439395},
+  keywords = {Building extraction,Buildings,change detection (CD),Data mining,Feature extraction,federated training,foundation model,Image segmentation,Remote sensing,remote sensing images,Task analysis,Training}
+}
+```

config.json

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+{
+  "architectures": [
+    "SwinModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    6,
+    2
+  ],
+  "drop_path_rate": 0.15,
+  "dtype": "float32",
+  "embed_dim": 96,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-05,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    3,
+    6,
+    12,
+    24
+  ],
+  "num_layers": 4,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "patch_size": 4,
+  "qkv_bias": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "transformers_version": "5.0.0.dev0",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8fc0eb4d63091ee665bdbb8a0c865d1716b96cf8cc1ba1ce60d8b60efd6241ab
+size 110335368

preprocessor_config.json

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+{
+  "do_resize": true,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "resample": 3,
+  "do_rescale": true,
+  "rescale_factor": 0.00392156862745098,
+  "do_normalize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ]
+}