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+
+CSI-CLIP License Notice
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+Copyright (c) 2026 GREAT contributors.
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README.md

@@ -1,3 +1,150 @@
 ---
 license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: feature-extraction
+tags:
+  - wireless
+  - channel-state-information
+  - channel-foundation-model
+  - contrastive-learning
+  - resnet
 ---
+
+# CSI-CLIP ResNet-50
+
+CSI-CLIP is a channel foundation model that aligns channel state information
+(CSI/CFR) and channel impulse response (CIR) representations through
+contrastive pre-training. This repository contains the official model-only
+ResNet-50 weights associated with
+[*A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency*](https://arxiv.org/abs/2502.11965).
+
+- Code: [GREAT-ISAC/CSI-CLIP](https://github.com/GREAT-ISAC/CSI-CLIP)
+
+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:2502.11965](https://arxiv.org/abs/2502.11965)
+- IEEE: [ICMLCN 2025](https://ieeexplore.ieee.org/abstract/document/11140262/)
+
+## Weight files
+
+Both files contain the same pre-trained model parameters. They do not contain
+an optimizer, scheduler, training data, or a downstream task head.
+
+| File | Type | Intended use |
+| --- | --- | --- |
+| `model.safetensors` | Model-only pre-trained weights | Recommended for safe standalone loading and feature extraction |
+| `model.pth` | Model-only PyTorch checkpoint with a `model` key | Compatibility with the existing fine-tuning scripts |
+
+These are foundation-model pre-training weights, not task-specific checkpoints
+for positioning, beam management, or LOS/NLOS classification. A downstream
+head must be trained before task-level inference.
+
+The original checkpoint used the legacy names `cfr_backbone`, `cir_backbone`,
+`proj_cfr`, and `proj_cir`. They were mapped to the names in the public
+`CSICLIP` class without changing the tensors. The public class additionally
+expects `logit_scale`; because the original checkpoint predates that parameter,
+it is initialized to the documented CLIP default of `log(1 / 0.07)`. This does
+not affect CSI encoder feature extraction. Exact SHA-256 values are recorded in
+`manifest.json`.
+
+## Input contract
+
+| Property | Value |
+| --- | --- |
+| Input shape | `[batch, 2, 256, 256]` |
+| Channel order | Real, imaginary |
+| Dtype | `float32` |
+| Normalization | Per-sample, per-channel min-max normalization to `[0, 1]` |
+| Normalization epsilon | `1e-8` |
+| CIR construction | IFFT of the normalized complex CSI along the last axis |
+| CSI embedding size | 256 |
+
+The same preprocessing must be used during training, fine-tuning, and
+inference. The repository implementation is authoritative; see
+`build_cir_cfr_pair` in `augmentations.py`.
+
+## Usage
+
+Install the code and its minimal dependencies:
+
+```bash
+git clone https://github.com/GREAT-ISAC/CSI-CLIP.git
+cd CSI-CLIP
+pip install -r requirements.txt
+```
+
+After downloading `model.safetensors`, run the model-loading smoke test:
+
+```bash
+python load_pretrained.py \
+  --checkpoint /path/to/model.safetensors
+```
+
+Expected output:
+
+```text
+CSI embedding shape: (1, 256)
+```
+
+Run feature extraction on a complex `cfr.npy` sample:
+
+```bash
+python load_pretrained.py \
+  --checkpoint /path/to/model.safetensors \
+  --input /path/to/scenario/cfr.npy \
+  --sample-index 0
+```
+
+## Training data
+
+The model was pre-trained on simulated DeepMIMO CSI covering multiple wireless
+scenarios. Generated training arrays are not included in this model repository.
+A **reproducible data-generation pipeline** will be released separately with
+the simulator configuration, preprocessing, and split definitions.
+
+## Intended use
+
+- Research on wireless/channel foundation models.
+- CSI representation and feature extraction.
+- Initialization for positioning, beam management, and LOS/NLOS classifiers.
+- 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.
+- Inputs with different antenna/subcarrier layouts require an explicitly
+  validated adaptation rather than an assumed reshape.
+- The released weights do not provide task predictions without a trained
+  downstream head.
+- The model is not intended for safety-critical deployment or commercial use.
+- Results depend on reproducing the documented preprocessing exactly.
+
+## License
+
+The original CSI-CLIP 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
+@inproceedings{jiang2025csi_clip,
+  title={A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency},
+  author={Jiang, Jun and Yu, Wenjun and Li, Yunfan and Gao, Yuan and Xu, Shugong},
+  booktitle={2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)},
+  pages={1--6},
+  year={2025},
+  doi={10.1109/ICMLCN64995.2025.11140262}
+}
+```
+
+## 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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+{
+  "architecture": "csi-clip-resnet50",
+  "embed_dim": 256,
+  "input_channels": 2,
+  "input_size": [
+    256,
+    256
+  ],
+  "normalization": "per-sample, per-channel min-max normalization",
+  "source_domain": "DeepMIMO"
+}

manifest.json

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+{
+  "architecture": "csi-clip-resnet50",
+  "contents": "model parameters only; no optimizer or scaler state",
+  "embed_dim": 256,
+  "input_channels": 2,
+  "input_size": [
+    256,
+    256
+  ],
+  "name": "CSI-CLIP",
+  "normalization": "per-sample, per-channel min-max normalization",
+  "source_checkpoint": "best.pth",
+  "source_domain": "DeepMIMO",
+  "source_sha256": "5c1eb1b6cfe349d8bd336c2158c7387e54cf7c9c6f676745bd95bd6aeaa83e35",
+  "weight_files": {
+    "model.pth": {
+      "bytes": 198095479,
+      "format": "PyTorch checkpoint",
+      "recommended": false,
+      "sha256": "03105d61289860ff626c8a774ffbafa72925a58d8b099f33129a1afc8dd656fa"
+    },
+    "model.safetensors": {
+      "bytes": 197997044,
+      "format": "safetensors",
+      "recommended": true,
+      "sha256": "a7aeb2270e4c761396f25aa4ff4da4a8bf99ec88fc6f620500d446be73335505"
+    }
+  }
+}

model.pth

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model.safetensors

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