---
license: mit
tags: [robotics, surface-roughness, grinding, contrastive-learning, RNC, segmentation]
---

# 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`.

Two independent models, answering two different questions about a weld bead being
ground off a metal plate:

| model | question | file |
|---|---|---|
| ROI detector | **where** must the robot grind? | `outputs/roi_cnn/roi_cnn.pt` |
| RNC grader | **how rough** is this surface? | `outputs/rnc_grader/rnc_sandpaper_grader.pt` |

## ROI detector (seam masking)

`outputs/roi_cnn/roi_cnn.pt` — masks which blocks of the plate are weld seam that
needs grinding, vs bare plate. A plain `state_dict` for `TinyNet`
(`scripts/p7_cnn_train.py`): 3 × (conv3×3 → BN → ReLU) at 16/32/64 channels with 2
max-pools, global-avg-pool, `Linear(64→1)`, sigmoid. **23,873 parameters.**

![ROI detector across all 12 passes](figures/roi_all_passes.png)

Green ∝ P(seam), red = annotated ROI polygon, blue = plate. Neutral-lit passes
(top) give a tight dense band; the blue-lit passes are sparser — that lighting
change is the model's hardest case.

Operating config — the detector is **fully convolutional over blocks, not over the
image**: slide a 24 px context window over the plate and paint the 12 px block at
its centre.

| | |
|---|---|
| input | 32×32 BGR, `/255`, = the 24 px context window resized |
| paint block | 12 px |
| threshold | **0.80** (best F1 0.703, P 0.68 / R 0.72); 0.5 → recall 0.91 |
| plate bbox | `[500, 412, 713, 519]` |

```python
import torch, torch.nn as nn

class TinyNet(nn.Module):
    def __init__(s):
        super().__init__()
        s.f = nn.Sequential(
            nn.Conv2d(3, 16, 3, padding=1), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(2),
            nn.Conv2d(16, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.AdaptiveAvgPool2d(1))
        s.head = nn.Linear(64, 1)
    def forward(s, x):
        return s.head(s.f(x).flatten(1)).squeeze(1)

net = TinyNet()
net.load_state_dict(torch.load("roi_cnn.pt", map_location="cpu"))
net.eval()
prob = torch.sigmoid(net(x))        # x: (N,3,32,32) in [0,1], BGR
```

Leave-one-pass-out (train on 11 passes, test the held-out one): pooled F1 0.632
@0.5, neutral passes F1 0.701, blue-lit passes F1 0.580. Cross-lighting robustness
comes from plate-restricted sampling, context windows, and colour/brightness
jitter augmentation. Full write-up:
[`ROI_DETECTOR.md`](https://github.com/BabaYaga840/grind/blob/main/approaches/visual_grind_grading/ROI_DETECTOR.md).

## 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
- `cache/*.npz` — p27–p38 embedding caches (multisession / SupCon / RNC
  experiments) for reproducing analyses without recompute.

Not hosted: `outputs/p7_samples.npz`, the ROI detector's training patches. Rebuild
with `scripts/p7_build_samples.py` (needs the raw rosbags) if you want to retrain.

## Scope note

The ROI detector works. The broader hypothesis this approach set out to test —
that image appearance encodes *cumulative* grinding, so pass index is recoverable
from a photo of the plate — came out **negative** on this dataset; the strong
correlations were a lighting/equipment confound. The sandpaper RNC grader above is
the texture-grounded replacement. See the repo README for that story.