Add MKD whitening models in safetensors (bitwise copies of manyids2/mkd_pytorch mkd-*-64.pth), LICENSE, card
6f6df73 verified | license: mit | |
| tags: | |
| - kornia | |
| - feature-description | |
| - local-descriptor | |
| # kornia/mkd | |
| Whitening models for **MKD** (Multiple Kernel Local Descriptor), used by | |
| [`kornia.feature.MKDDescriptor`](https://kornia.readthedocs.io/en/latest/feature.html). | |
| MKD describes a local patch with kernel embeddings of its gradients. The descriptor is then whitened with statistics | |
| learned on the PhotoTourism patch dataset. These files hold those statistics for each kernel type and training | |
| set. | |
| **Original repo:** [manyids2/mkd_pytorch](https://github.com/manyids2/mkd_pytorch) | |
| ## Weights | |
| | File | Kernel type | | |
| |------|-------------| | |
| | `mkd-cart-64.safetensors` | `cart` | | |
| | `mkd-polar-64.safetensors` | `polar` | | |
| | `mkd-concat-64.safetensors` | `concat` (polar and cart) | | |
| Each file holds 18 float32 tensors named `<training_set>.<whitening>.<statistic>`: | |
| - `training_set` is one of `liberty`, `notredame` and `yosemite`; | |
| - `whitening` is `pca` (unsupervised) or `lw` (learned, supervised); | |
| - `statistic` is one of `mean`, `eigvecs` and `eigvals`. | |
| The upstream pickles nest the same tensors as `{training_set: {whitening: {statistic: tensor}}}`. | |
| ## Provenance | |
| Each `.safetensors` file holds the tensors of the upstream pickle with the same stem, unchanged: the same dtypes, | |
| shapes and bits. The kornia maintainers converted them (converter revision `0c3db846b2`). | |
| | File | Upstream source | Source sha256 | sha256 | | |
| |------|-----------------|---------------|--------| | |
| | `mkd-cart-64.safetensors` | [`mkd-cart-64.pth`](https://github.com/manyids2/mkd_pytorch/raw/master/mkd_pytorch/mkd-cart-64.pth) | `a9f907bbc0eacd19e31228f839ed12ff6ecbbbfafa2bbde1266788c722c38d56` | `696e936d9848af36e872794b0b99d08125ef5a76ce5a99feae3dfd6e76dd8eba` | | |
| | `mkd-polar-64.safetensors` | [`mkd-polar-64.pth`](https://github.com/manyids2/mkd_pytorch/raw/master/mkd_pytorch/mkd-polar-64.pth) | `8c5c497a71bb186477fddd722eaedd9a68f5d17ea0e632d226a05a6d19994754` | `30e768eba7b1d03133f8f2a316e31900faccae460ef2fe694ec411f29965470d` | | |
| | `mkd-concat-64.safetensors` | [`mkd-concat-64.pth`](https://github.com/manyids2/mkd_pytorch/raw/master/mkd_pytorch/mkd-concat-64.pth) | `e31112000009e0d6e159dd7726aeea9f6a7d746cfe7d59b59bfb86c570667641` | `e9d96de020c89f8f59ee073c972278671b6eb997d17e168d4242e59a8cd4fe88` | | |
| The upstream files store each `pca.eigvecs` as a transposed view. A safetensors file stores row-major bytes, so the | |
| values here are identical but the tensor is contiguous. For every whitening variant and training set, an | |
| `MKDDescriptor` gives bitwise-identical outputs from either file once its parameters are contiguous. | |
| ## License | |
| MIT. [`LICENSE`](LICENSE) is copied verbatim from manyids2/mkd_pytorch. Its copyright line names the Python | |
| Packaging Authority, carried over from a project template; the package's `setup.py` names Arun Mukundan as its | |
| author. | |
| ## Citation | |
| ```bibtex | |
| @article{mukundan2018understanding, | |
| title = {Understanding and improving kernel local descriptors}, | |
| author = {Mukundan, Arun and Tolias, Giorgos and Bursuc, Andrei and J{\'e}gou, Herv{\'e} and Chum, Ond{\v{r}}ej}, | |
| journal = {International Journal of Computer Vision}, | |
| year = {2018} | |
| } | |
| ``` | |