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+CSI-MAE License Notice
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README.md

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 ---
 license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: feature-extraction
+tags:
+  - wireless
+  - channel-state-information
+  - channel-foundation-model
+  - masked-autoencoder
+  - vision-transformer
 ---
+
+# CSI-MAE ViT-Base/16 and ViT-Large/16
+
+CSI-MAE is a masked-autoencoder channel foundation model that learns reusable
+CSI representations through masked channel reconstruction. This repository
+contains the official model-only ViT-Base/16 and ViT-Large/16 pre-training
+weights associated
+with [*CSI-MAE: A Masked Autoencoder-based Channel Foundation Model*](https://arxiv.org/abs/2601.03789).
+
+- Code: [GREAT-ISAC/CSI-MAE](https://github.com/GREAT-ISAC/CSI-MAE)
+
+Released by **[GREAT Wireless AI](https://github.com/GREAT-ISAC)**. The
+`GREAT-ISAC` GitHub handle is retained for continuity with existing papers and
+community links.
+- Paper: [arXiv:2601.03789](https://arxiv.org/abs/2601.03789)
+
+## Weight files
+
+The release contains epoch-300 Base and Large checkpoints pretrained on the
+simulated Sionna/3GPP channel corpus. These are not the separate DeepMIMO
+experimental checkpoints. None of the files contains an optimizer, AMP scaler,
+epoch/resume state, training data, or a downstream task head.
+
+| File | Type | Intended use |
+| --- | --- | --- |
+| `csi-mae-base.safetensors` | Base model-only pre-trained weights | Recommended Base weight for safe standalone loading and feature extraction |
+| `csi-mae-base.pth` | Base model-only PyTorch checkpoint with a `model` key | Base compatibility weight for existing scripts |
+| `csi-mae-large.safetensors` | Large model-only pre-trained weights | Recommended Large weight for safe standalone loading and feature extraction |
+| `csi-mae-large.pth` | Large model-only PyTorch checkpoint with a `model` key | Large compatibility weight for existing scripts |
+
+These are foundation-model pre-training weights, not final checkpoints for
+positioning, channel feedback, or channel extrapolation. The corresponding
+downstream architecture must be initialized from these weights and then
+adapted or fine-tuned. Exact SHA-256 values are recorded in `manifest.json`.
+
+## Model and input contract
+
+| Property | Value |
+| --- | --- |
+| Architecture | MAE ViT-Base/16 or ViT-Large/16 |
+| Input shape | `[batch, 2, 256, 256]` |
+| Channel order | Real, imaginary |
+| Dtype | `float32` |
+| Pre-training channel mean | `[-5.5445e-10, -5.5445e-10]` |
+| Pre-training channel variance | `[5.7916e-10, 5.7916e-10]` |
+| Normalization | Channel-wise standardization using the fixed statistics above |
+| Patch size | 16 |
+| Pre-training mask ratio | 0.75 |
+| Normalized pixel loss | Enabled |
+
+The same preprocessing must be used during training, fine-tuning, and
+inference. The implementation in `dataset.py` is authoritative.
+
+Because normalized-pixel loss was enabled during pre-training, the decoder's
+patch predictions are normalized reconstruction targets. The loading example
+is therefore a forward/reconstruction smoke test, not recovery of CSI in its
+original physical scale. Physical-scale reconstruction additionally requires
+unpatchifying and applying the appropriate inverse patch and sample
+normalization.
+
+## Usage
+
+Install the code and its minimal dependencies. CSI-MAE requires the pinned
+`timm==0.3.2`; newer `timm` releases remove the `qk_scale` API used by the
+released implementation.
+
+```bash
+git clone https://github.com/GREAT-ISAC/CSI-MAE.git
+cd CSI-MAE
+pip install -r requirements.txt
+```
+
+After downloading a weight, run the strict loading and forward smoke test. The
+`--model` value must match the selected checkpoint:
+
+```bash
+python load_pretrained.py \
+  --model base \
+  --checkpoint /path/to/csi-mae-base.safetensors
+```
+
+Expected output includes:
+
+```text
+Reconstruction shape: (1, 256, 512)
+Mask shape: (1, 256)
+```
+
+Run the same check on a complex `cfr.npy` sample:
+
+```bash
+python load_pretrained.py \
+  --model large \
+  --checkpoint /path/to/csi-mae-large.safetensors \
+  --input /path/to/scenario/cfr.npy \
+  --sample-index 0
+```
+
+## Training data
+
+The Base and Large models were pre-trained on simulated Sionna/3GPP CSI. The
+separate DeepMIMO experiments are not the source of these published weights.
+Generated training arrays are not included in this model repository. A
+**reproducible data-generation pipeline** will be released separately with the
+simulator configuration, preprocessing, normalization statistics, and split
+definitions.
+
+## Intended use
+
+- Research on wireless/channel foundation models.
+- Masked CSI representation learning and reconstruction studies.
+- Initialization for positioning, channel feedback, and channel extrapolation.
+- Non-commercial evaluation and reproducibility studies.
+
+## Limitations and out-of-scope use
+
+- The model was trained on simulated data; performance on measured channels is
+  not guaranteed.
+- The public architecture assumes two-channel `256 x 256` inputs. Other antenna
+  or subcarrier layouts require an explicitly validated adaptation.
+- Decoder outputs are not physical-scale complex CSI without the documented
+  inverse-processing steps.
+- The released weights do not provide final downstream predictions without
+  adaptation or fine-tuning.
+- The model is not intended for safety-critical deployment or commercial use.
+
+## License
+
+The original CSI-MAE code, these model weights, and the accompanying original
+data-generation scripts are released under the
+[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/)
+license (**CC BY-NC 4.0**). Attribution is required and commercial use is not
+permitted without prior written authorization from the copyright holders.
+Third-party software, simulators, datasets, and scenario assets remain subject
+to their respective licenses.
+
+## Citation
+
+```bibtex
+@article{jiang2026csimae,
+  title={CSI-MAE: A Masked Autoencoder-based Channel Foundation Model},
+  author={Jiang, Jun and Ruan, Xiaolong and Xu, Shugong},
+  journal={arXiv preprint arXiv:2601.03789},
+  year={2026}
+}
+```
+
+## Acknowledgement
+
+CSI-MAE is adapted from the public
+[Masked Autoencoders](https://github.com/facebookresearch/mae)
+implementation. The original attribution notices are retained in the source
+repository.
+
+## Contact
+
+For questions, contact Jun Jiang at
+[Jun.Jiang25@student.xjtlu.edu.cn](mailto:Jun.Jiang25@student.xjtlu.edu.cn).

config.json

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+{
+  "architectures": {
+    "base": "mae_vit_base_patch16",
+    "large": "mae_vit_large_patch16"
+  },
+  "input_channels": 2,
+  "input_size": [
+    256,
+    256
+  ],
+  "mask_ratio": 0.75,
+  "norm_pix_loss": true,
+  "normalization": {
+    "mean": [
+      -5.5445e-10,
+      -5.5445e-10
+    ],
+    "type": "channel-wise standardization",
+    "variance": [
+      5.7916e-10,
+      5.7916e-10
+    ]
+  },
+  "patch_size": 16,
+  "source_domain": "Sionna/3GPP simulated channel corpus"
+}

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manifest.json

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+{
+  "architectures": {
+    "base": "mae_vit_base_patch16",
+    "large": "mae_vit_large_patch16"
+  },
+  "contents": "model parameters only; no optimizer or scaler state",
+  "input_channels": 2,
+  "input_size": [
+    256,
+    256
+  ],
+  "mask_ratio": 0.75,
+  "name": "CSI-MAE",
+  "norm_pix_loss": true,
+  "normalization": {
+    "mean": [
+      -5.5445e-10,
+      -5.5445e-10
+    ],
+    "type": "channel-wise standardization",
+    "variance": [
+      5.7916e-10,
+      5.7916e-10
+    ]
+  },
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