Add model card

README.md

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+---
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: feature-extraction
+tags:
+  - tactile
+  - robotics
+  - gelsight
+  - masked-autoencoder
+  - multimodal
+  - representation-learning
+---
+
+# HTT — Heterogeneous Tactile Transformer
+
+A multimodal tactile representation model. One shared transformer backbone
+encodes four different tactile sensors into a common **192-dimensional**
+embedding space, pretrained with masked-autoencoder reconstruction and
+cross-modal alignment. Feed a raw sensor reading, get a feature vector for any
+downstream head (classification, force / slip estimation, policy learning).
+
+| Modality | Type | Raw input |
+|---|---|---|
+| `gsmini` | vision (GelSight Mini) | uint8 image `[224, 224, 3]` |
+| `9dtact` | vision (9DTact) | uint8 image `[224, 224, 3]` |
+| `xela`   | taxel array | float `[T, 72]` |
+| `tac02`  | taxel array | float `[T, 66]` |
+
+## Checkpoint
+
+| | |
+|---|---|
+| File | `htt_4sensors_best.pth` (~69 MB) |
+| Contents | `model_state_dict` = 4 encoders + shared 9-layer trunk + 4 decoders (17.1 M params) |
+| Embedding dim | 192 |
+| SHA-256 | `024f4c3a067168197d0a6996bbca5c03e744ed5abd1d35a666dbf78e7ac673f0` |
+
+Slim inference/finetune checkpoint (optimizer / predictor states dropped).
+
+## Usage
+
+Use it with the **HTT** package (contains the architecture, preprocessing, and
+examples). Download the weights into `checkpoints/`:
+
+```bash
+hf download AllenBi21/HTT htt_4sensors_best.pth --local-dir checkpoints
+```
+
+```python
+import numpy as np
+from htt import HTT
+
+model = HTT(modality="gsmini")                       # loads checkpoints/htt_4sensors_best.pth
+frame = np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8)   # your sensor frame
+emb = model(frame)                                   # -> torch.Tensor [1, 192]
+```
+
+Read the raw-input contract before feeding your own data — the model returns
+garbage on out-of-distribution inputs.
+
+## License
+
+Released under **CC BY-NC 4.0** (non-commercial). Portions are derived from
+Meta's V-JEPA / DINOv2 (Apache-2.0 and CC-BY-NC-4.0).