Initial release

LICENSE

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+Copyright 2026 Sharpa Group
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README.md

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 ---
 license: apache-2.0
+library_name: pytorch
+pipeline_tag: feature-extraction
+tags:
+  - tactile-sensing
+  - feature-extraction
+  - robotics
+  - pytorch
+  - convnextv2
 ---
+
+# SharpaWave Deform Encoder
+
+`DeformEncoder` converts a preprocessed single-channel scalar deformation image
+into a compact learned tactile feature with shape `[B, 512, 1, 1]`. The feature
+can be flattened to `[B, 512]` for downstream tasks.
+
+The unified checkpoint also includes `DeformDecoder` parameters. The decoder
+and `DeformAutoencoder` are provided only to demonstrate deformation
+reconstruction from the compact feature.
+
+## Tensor Shapes
+
+| Operation | Input | Output |
+| --- | --- | --- |
+| Encoder | `[B, 1, 240, 240]` | `[B, 512, 1, 1]` |
+| Flatten feature | `[B, 512, 1, 1]` | `[B, 512]` |
+| Autoencoder | `[B, 1, 240, 240]` | `[B, 1, 240, 240]` |
+
+The input is a preprocessed scalar deformation image, not a raw RGB camera
+image.
+
+## Usage
+
+Download the checkpoint and use `load_encoder()` from the source repository:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+from sharpawave_deform_encoder import load_encoder
+
+checkpoint = hf_hub_download(
+    repo_id="Sharpa-Robotics/sharpawave-deform-encoder",
+    filename="sharpawave_deform_autoencoder.safetensors",
+)
+encoder = load_encoder(checkpoint, "cpu")
+
+deform = torch.zeros(1, 1, 240, 240)
+with torch.inference_mode():
+    feature = encoder(deform)  # [1, 512, 1, 1]
+```
+
+Source code: <https://github.com/sharpa-robotics/sharpawave-deform-encoder>
+
+## Checkpoint
+
+The single SafeTensors file contains both encoder and reconstruction-only
+decoder parameters. `load_encoder()` reads only the encoder tensors;
+`load_autoencoder()` loads the complete demonstration model.
+
+The published checkpoint was trained from random initialization without
+upstream pretrained weights.
+
+The SHA-256 digest is recorded in `SHA256SUMS`.
+
+## Limitations
+
+The encoder expects the documented 240-by-240 scalar input representation.
+
+## License
+
+Developed by Sharpa Group. Licensed under Apache License 2.0. See `LICENSE` and
+`THIRD_PARTY_NOTICES.md`.

SHA256SUMS

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+031ba006e2c6ab4446fedd8561cc478c8757ea18556054826ee1ad847ddad841  sharpawave_deform_autoencoder.safetensors

THIRD_PARTY_NOTICES.md

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+# Third-Party Notices
+
+This project depends on the following third-party software but does not
+redistribute its source code or binary packages. Each dependency remains
+subject to its own license terms.
+
+## PyTorch
+
+- License: BSD-3-Clause
+- Copyright: PyTorch contributors
+- License text: <https://github.com/pytorch/pytorch/blob/main/LICENSE>
+
+## timm
+
+- License: Apache-2.0
+- Copyright: Ross Wightman and timm contributors
+- License text: <https://github.com/huggingface/pytorch-image-models/blob/main/LICENSE>
+
+## NumPy
+
+- License: BSD-3-Clause
+- Copyright: 2005-2024, NumPy Developers
+- License text: <https://github.com/numpy/numpy/blob/main/LICENSE.txt>
+
+## OpenCV
+
+- License: Apache-2.0
+- Copyright: OpenCV contributors
+- License text: <https://github.com/opencv/opencv/blob/4.x/LICENSE>
+
+The inference checkpoint was trained from random initialization without
+upstream pretrained weights. It uses the ConvNeXt V2 Pico architecture
+implemented by timm.

sharpawave_deform_autoencoder.safetensors

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