quantized duplicated precisions.json, which is authoritative for the
quantized/non-quantized partition. default expressed a per-repository
recommendation nothing consumed: the library resolves its own per-platform
default. Both are gone from the schema.
Adds library_name to the model card, and pipeline_tag where it was missing.
Brings the config and card licenses into agreement, and repairs the variant
declarations that did not match the files actually published here.
Put the backend directories back at the repo root
yolo26 and yolo26-seg use a top level size directory because they publish
five sizes. This repo publishes one, so it stays flat, like blazeface and
rfdetr-nano-detector. The corrected configs from the previous commit are
unaffected.
Move the size-n exports under n/ and rewrite the backend configs
Matches the yolo26 and yolo26-seg layout, where the model size is the top
level directory and the input resolution stays in the filename.
The xnnpack config described a single yolo26_pose_n_xnnpack_fp32.pte with
forward_384/512/640 methods. That file was never published here: the repo
ships three files, each with a single forward. Core ML had no config at all.
The previous removal commit said earlier tags still contain the MLX artifacts. That is wrong: MLX for this model was only ever published on main / v0.10.0, and no tag at v0.9.0 or below ever carried it. The files remain recoverable from git history. v0.9.0 and below are, and have been, untouched.
Remove MLX artifacts: 7.5-19.6x slower than Core ML on device
MLX measured substantially slower than Core ML on an iPhone 16 for every convolutional vision model tested (efficientnet-v2-s 19.6x, yolo26-s 16.0x, yolo26-seg-s 7.5x, yolo26-pose-n 10.0x). MLX weight quantization also cannot shrink a conv model, since it only rewrites nn.Linear/embedding modules.
The .pte and config.json are removed; each mlx/ dir keeps a NOTES.md with the measurements. The exporter is retained in export-scripts so this can be re-tested against a future ExecuTorch/MLX release.
Earlier tags (v0.9.0 and below) are untouched and still contain the MLX artifacts.
Rebuild mlx export with fork toolchain (ExecuTorch 1.4.1)
