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README.md

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-license: mit
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+---
+license: mit
+---
+
+# Full-Volume Inference without Performance Degradation
+
+### 📝 Paper Title
+**Memory-Efficient Full-Volume Inference for Large-Scale 3D Dense Prediction without Performance Degradation** in **Communications Engineering** (2025) by Jintao Li and Xinming Wu
+
+This work introduces a scalable inference framework that enables **whole-volume 3D prediction without accuracy loss**, even on extremely large seismic datasets.  
+The approach restructures high-memory operators during inference only (no retraining required), allowing models to process volumes up to `1024³` directly on modern GPUs.
+
+The method is particularly useful for seismic interpretation tasks such as fault detection, RGT estimation, implicit structural modeling, and geological feature segmentation.
+
+
+Homepage: [https://github.com/JintaoLee-Roger/torchseis](https://github.com/JintaoLee-Roger/torchseis)
+
+---
+
+## 🔑 Key Features
+
+✔ **Retraining-free**: works with existing pretrained models  
+✔ **Whole-volume inference**: no ghost boundaries or stitching artifacts  
+✔ **Memory-efficient**: reduces decoder/stem memory footprint  
+✔ **Faster runtime**: avoids slow CuDNN kernel fallback  
+✔ **Operator-level optimization**: convolution, interpolation, and normalization  
+
+---
+
+## 🚀 Basic Usage
+
+Below is an example using `FaultSeg3D` under the TorchSeis framework:
+
+```python
+import torch
+from torchseis import models as zoo
+
+# 1. Load model
+model = zoo.FaultSeg3d()
+
+# 2. Load pretrained weights
+state = torch.load('faultseg3d-2020-70.pth', weights_only=True)
+model.load_state_dict(state)
+
+# 3. Convert to GPU
+model = model.half().eval().cuda()
+
+# 4. Prepare input volume 
+data = torch.from_numpy(f3d[np.newaxis, np.newaxis].copy()).half().cuda()
+
+# 5. Full-volume inference (no tiling)
+with torch.no_grad():
+    pred = model(data, rank=3).cpu().numpy()
+```
+
+> `rank=3` means that using strategy 4 in the paper.
+
+---
+
+## 🧠 Model Zoo Compatibility
+
+The full-volume inference method is compatible with the following TorchSeis models, which are available in the [torchseis-efficiency-infer](https://huggingface.co/shallowclose/torchseis-efficiency-infer) on **Hugging Face Hub**. Besides, these models can also be accessed via **Baidu Netdisk**:
+
+通过网盘分享的文件：torchseis-efficiency-infer
+链接: [https://pan.baidu.com/s/1ygUPYIO0S1AvsU4-IMz4pg?pwd=y7cn](https://pan.baidu.com/s/1ygUPYIO0S1AvsU4-IMz4pg?pwd=y7cn) 提取码: y7cn
+
+
+
+| Model            | Task                                           | Source           |
+| ---------------- | ---------------------------------------------- | ---------------- |
+| `FaultSeg3d`     | Fault segmentation                             | [Wu, et, al., 2019, Geophysics](https://library.seg.org/doi/abs/10.1190/geo2018-0646.1)   |
+| `FaultSeg3dPlus` | Fault segmentation                             | [Li, et, al., 2024, Geophysics](https://library.seg.org/doi/abs/10.1190/geo2022-0778.1)   |
+| `FaultSSL`       | Fault segmentation                             | [Dou, et, al., 2024, Geophysics](https://library.seg.org/doi/abs/10.1190/geo2023-0550.1)   |
+| `Bi21RGT3d`      | Relative geological time (RGT) Estimation      | [Bi, et, al., 2021, JGR-SE](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021882)   |
+| `DeepISMNet`     | Implicit structural modeling                   | [Bi, et, al., 2022, GMD](https://gmd.copernicus.org/articles/15/6841/2022/)  |
+| `ChannelSeg3d`   | Channel segmentation                           | [Gao, et, al., 2021, Geophysics](https://library.seg.org/doi/abs/10.1190/geo2020-0572.1)   |
+| `Wang25Channel`  | Channel segmentation                           | [Wang, et, al., 2025, ESSD](https://essd.copernicus.org/articles/17/3447/2025)   |
+| `KarstSeg3d`     | Paleokarst detection                           | [Wu, et, al., 2020, JGR-SE](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020JB019685)  |
+| ~`GEM`~[^1]          | ~Geological Everything Model~                  | [Dou, et, al. 2025](https://arxiv.org/abs/2507.00419) |
+| ~`SegFormer3D`~[^2]  | ~3D medical segmentation (Transformer)~        | [Perera, et, al., 2025, CVPR](https://arxiv.org/abs/2404.10156)   |
+
+
+---
+
+[^1]: **`GEM` Model Note:** The paper is currently under peer review. We will await the official release of the model weights by the authors before considering distribution.
+
+[^2]: **`SegFormer3D` Model Note:** As the code variables were not changed, users are advised to use the original author's weights directly. We are uncertain about the rights to redistribute these weight files.
+
+---
+
+## 📊 Results Summary
+
+The performance evaluations, visual comparisons, and numerical errors reported in our Communications Engineering (2025) paper are fully reproducible.
+
+All plotting scripts and evaluation utilities can be found under `scripts/infer25ce/` in the repository: [torchseisJintaoLee-Roger/torchseis](https://github.com/JintaoLee-Roger/torchseis).
+
+This directory contains:
+- **Fig. 3** – Runtime and memory usage comparisons across models
+- **Fig. S1** – Runtime and memory usage comparisons across models
+- **Table S1** – Numerical error statistics (full vs. chunked inference)
+
+The raw files used to compose Figure 5 is on the zenodo: [https://doi.org/10.5281/zenodo.17810071](https://doi.org/10.5281/zenodo.17810071)
+
+---
+
+## 📚 Citation
+
+If this work or the inference strategy is used in your research, please cite:
+
+```bibtex
+@article{li2025infer,
+  title={Memory-Efficient Full-Volume Inference for Large-Scale 3D Dense Prediction without Performance Degradation},
+  author={Li, Jintao and Wu, Xinming},
+  journal={Communications Engineering},
+  year={2025}
+}
+```
+
+---
+
+For questions or contributions, please submit an issue or pull request via the main repository.