| --- |
| license: mit |
| tags: [robotics, surface-roughness, grinding, contrastive-learning, RNC] |
| --- |
| |
| # grind — visual_grind_grading checkpoints |
|
|
| Trained artifacts for the `visual_grind_grading` approach of the |
| [grind](https://github.com/BabaYaga840/grind) repo. Gitignored there; hydrate a |
| fresh clone with `./pull_checkpoints.sh`. |
|
|
| ## Grading model (RNC) |
| `outputs/rnc_grader/rnc_sandpaper_grader.pt` — the RNC sandpaper Ra grader. |
| `torch.load` gives a dict: `Enc` encoder `state_dict` (3-conv CNN, dim 32) + |
| per-grit `anchors` + `grit_classes` + `Ra_by_grit`. Inference: |
|
|
| ```python |
| import torch, torch.nn.functional as F, numpy as np |
| c = torch.load("rnc_sandpaper_grader.pt", weights_only=False) |
| # rebuild Enc (see scripts/shared_grading.py: class Enc), load c["state_dict"] |
| def illum_norm(x): m=x.mean((2,3),keepdim=True); s=x.std((2,3),keepdim=True)+1e-4; return (x-m)/s |
| z = F.normalize(net(illum_norm(x)), dim=1).cpu().numpy() # x: (N,3,64,64) [0,1] |
| w = np.exp(z @ c["anchors"].T / c["tau"]); w /= w.sum(1, keepdims=True) |
| grade = w @ c["grit_classes"].astype("float32") # soft ordinal, 0..6 |
| ``` |
| Validated leave-angle-out rho ~0.93 vs true grit (real, texture-grounded). |
|
|
| ## Other artifacts |
| - `outputs/roi_cnn/roi_cnn.pt` — TinyNet ROI CNN weights (grinding ROI, state_dict). |
| - `cache/*.npz` — p27–p38 embedding caches (multisession / SupCon / RNC |
| experiments) for reproducing analyses without recompute. |
| |
