| --- |
| license: mit |
| library_name: pytorch |
| pipeline_tag: image-segmentation |
| tags: |
| - pose-estimation |
| - marine-biology |
| - pycnopodia |
| - sunflower-star |
| extra_gated_prompt: > |
| These are trained DeepStarNoodles weights (StarSeg, Hydra, Noodle Tracer, |
| temporal VAE). |
| Access is gated so we know who has the locked recipe. DINOv3 backbone |
| weights are not included; download those from Meta under the DINOv3 License. |
| extra_gated_fields: |
| Affiliation: text |
| I will cite DeepStarNoodles and DINOv3 where I use them: checkbox |
| --- |
| |
| # DeepStarNoodles weights |
|
|
| Locked inference and retrain weights for [DeepStarNoodles](https://github.com/weertman/DeepStarNoodles). |
|
|
| | File | Role | SHA-256 | |
| | --- | --- | --- | |
| | `starseg/starseg_best.pt` | StarSeg detector (YOLOv8) | `0252e49d3b201f5fe557536d37648ff6ff8ac1ca2c303ec7b5c707f1973a9414` | |
| | `hydra/epoch_060.pt` | 18-channel Hydra parent | `b3423e9bab886777e04825945434b8d42609b3c3fde0cd270d2fe2668c411eec` | |
| | `hydra/endpoint_mse_best.pt` | Released dense Hydra | `dcf7140c9ef0129d8f66b33b05af6d0ebb064f84b41ce61e51e67061799e3f02` | |
| | `tracer/noodle_tracer_fullcorpus_seed20261011_calibrated.pt` | Noodle Tracer (released, full corpus) | `f39dc82b9bc5d38e2053c2535ca30055a9d03140a713dbb991cb7058130d5287` | |
| | `tracer/noodle_tracer_seed20260807_endpoint_mse_calibrated.pt` | Previous Noodle Tracer (Sleepy-only, August 2026) | `bb7f1b5da4f998006163b0bfbba005b417dfd6e8ab313d8e3869b7224fcc5c6c` | |
| | `temporal_vae/dnt_progress_temporal_vae_60s_sleepy.pt` | Optional 60-second progress temporal VAE | `182de7d66d3b670c94e25d4f78070d7ffa5851acd76200cc7c059849b1abddc9` | |
|
|
| ## Released Noodle Tracer (October 2026) |
|
|
| The released tracer was trained on all 4,062 annotated training images, from every source family (not only Sleepy Stars). It runs on `hydra/endpoint_mse_best.pt` with an invalid-seed threshold of 0.6, stored in the checkpoint. It was selected on validation by a rule fixed in advance, then scored once on the held-out test splits under an analysis lock: |
|
|
| | Test split | Arm F1 | Group-macro F1 | Precision | Recall | |
| | --- | --- | --- | --- | --- | |
| | Sleepy Stars (63 images, 5 groups) | 0.868 | 0.855 | 0.912 | 0.828 | |
| | Other source families (439 images, 178 groups) | 0.854 | 0.836 | 0.888 | 0.822 | |
|
|
| Against the previous Sleepy-only tracer on the same images, arm F1 rose by +0.030 (95% CI +0.005 to +0.054) on Sleepy Stars and by +0.017 (+0.009 to +0.026) on the other families. The previous tracer stays here for reproducing the August 2026 release. See [`docs/published-tracer.md`](https://github.com/weertman/DeepStarNoodles/blob/main/docs/published-tracer.md) for the selection record and caveats. |
|
|
| First-party weights are MIT, matching the GitHub code. StarSeg inference uses Ultralytics (AGPL-3.0). The Hydra checkpoints include the fine-tuned DINOv3 ConvNeXt-Base backbone weights, so inference needs nothing else. Meta's original pretrained file `dinov3_convnext_base_pretrain_lvd1689m-801f2ba9.pth` (SHA-256 `801f2ba95801bdcc9a8b77e293d38b35b355dbc813ef609cd82e18c3a185e8da`) is **not** in this repo and is only needed to train Hydra from scratch; it is distributed by Meta under the DINOv3 License. |
|
|
| The temporal VAE is an inference-only export of the frozen DeepNoodleTracker |
| checkpoint at step 10,250. It expects one support-weighted 64×64 progress raster |
| per second and produces a 64-D posterior mean from a centered window spanning |
| −30 through +29 seconds. It was trained from Hydra checkpoint |
| `3f691daa14a891a5f9ad163355d969f8922892c662d4d9b7864a54fd815eecee`. |
| The currently released Hydra checkpoint has the same channel contract, but its |
| output distribution has not yet been scientifically revalidated with this VAE. |
| Use the released VAE as the default reproducible recipe or fine-tune a new model |
| through the configurable representation API. |
|
|
| ## Download into a DeepStarNoodles checkout |
|
|
| ```bash |
| hf download weertman/deepstarnoodles --local-dir MODELS |
| ``` |
|
|
| Then run `SCRIPTS/validate_artifacts.py --roles runtime-detector released-dense released-tracer`. Add the `pretrained-backbone` role only if you placed Meta's DINOv3 file at `MODELS/dinov3/dinov3_convnext_base_pretrain_lvd1689m-801f2ba9.pth` for Hydra training. The check skips Ultralytics `yolo26l-seg.pt` unless you add it for StarSeg from-scratch training. |
|
|
