| --- |
| library_name: pytorch |
| pipeline_tag: feature-extraction |
| tags: |
| - synthetic-aperture-radar |
| - sar |
| - remote-sensing |
| - self-supervised-learning |
| - masked-image-modeling |
| - jepa |
| - vision-transformer |
| --- |
| |
| # phi-JEPA: Where and What to Reconstruct in Physics-Informed Radar Pre-training? |
|
|
| This is the official model repository for **phi-JEPA**, a physics-informed masked autoencoder for self-supervised synthetic aperture radar (SAR) image pre-training. |
|
|
| > **Release status.** This repository currently provides the pretrained checkpoint used in our main experiments. The pre-training code and downstream evaluation code are not included in the current release. |
|
|
| ## Model Description |
|
|
| Existing masked image modeling methods for SAR imagery commonly rely on random masking and low-level reconstruction targets. phi-JEPA introduces two SAR-oriented designs: |
|
|
| 1. **Scattering-Center-Aware Masked Modeling (SCM).** A physical scattering prior reconstructed from attributed scattering centers guides masking toward target-related regions while maintaining a high overall masking ratio. |
| 2. **SAR-Specific Semantic Feature Prediction (SFP).** A frozen SARCLIP ViT-B/16 image encoder provides patch-level semantic targets, shifting the pre-training objective from low-level appearance reconstruction to semantic representation learning. |
|
|
| The released ViT-B/16 checkpoint is pretrained on ATRNet-STAR and is intended for downstream SAR representation transfer. |
|
|
| ## Released Checkpoint |
|
|
| | Model | Backbone | Pre-training dataset | Epochs | File | SHA-256 | |
| |---|---|---|---:|---|---| |
| | phi-JEPA | ViT-B/16 | ATRNet-STAR | 100 | [phi_jepa_vitb16_e100.pth](https://huggingface.co/kiki-orb/phi-JEPA/blob/main/main_experiments/phi_jepa_vitb16_e100.pth) | `f94f048366528815c15a51a060ea27d923db65ae2c6434931121cfd7af72f095` | |
|
|
|
|
| ## Download |
|
|
| Using the Hugging Face CLI: |
|
|
| ```bash |
| hf download kiki-orb/phi-JEPA \ |
| main_experiments/phi_jepa_vitb16_e100.pth \ |
| --local-dir ./checkpoints |
| ``` |
|
|
| Using Python: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| checkpoint_path = hf_hub_download( |
| repo_id="kiki-orb/phi-JEPA", |
| filename="main_experiments/phi_jepa_vitb16_e100.pth", |
| ) |
| print(checkpoint_path) |
| ``` |
|
|
| ## Pre-training Configuration |
|
|
| | Configuration | Value | |
| |---|---| |
| | Backbone | ViT-Base | |
| | Patch size | 16 x 16 | |
| | Pre-training dataset | ATRNet-STAR (SOC, 40 vehicle categories) | |
| | Optimizer | AdamW | |
| | Base learning rate | 1e-3 | |
| | Weight decay | 0.05 | |
| | Batch size | 128 | |
| | Pre-training epochs | 100 | |
| | Warmup | 20 epochs | |
| | Learning-rate schedule | Cosine annealing | |
| | Overall masking ratio | 80% | |
| | Target-region masking ratio | 50% | |
| | Feature alignment objective | Cosine loss | |
| | Semantic teacher | Frozen SARCLIP ViT-B/16 image encoder | |
