grind — visual_grind_grading checkpoints
Trained artifacts for the visual_grind_grading approach of the
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.
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] |
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.
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:
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.