| --- |
| license: apache-2.0 |
| library_name: pytorch |
| pipeline_tag: image-classification |
| tags: |
| - robotics |
| - capture-qa |
| - hand-detection |
| - edge |
| datasets: |
| - lerobot/droid_1.0.1 |
| - imageomics/KABR |
| model-index: |
| - name: referee-hands-s |
| results: |
| - task: |
| type: image-classification |
| name: Hand/gripper visibility (clip-level) |
| dataset: |
| type: referee-lab/hands-visible |
| name: referee-lab/hands-visible |
| split: full |
| metrics: |
| - type: accuracy |
| name: Provisional development-sheet clip accuracy |
| value: 0.9778 |
| --- |
| |
| # referee-hands-s |
|
|
| A 1.5M-parameter MobileNetV3-small classifier for hand/gripper visibility in |
| robot-training footage. 96px inputs, sized for microcontroller-class deployment. |
|
|
| The published 97.8% result is from a 45-clip, in-domain development sheet that |
| was repeatedly consulted during development and has not yet passed the declared |
| independent two-grader gate. It is reproducible development evidence, not a |
| frozen external benchmark. |
|
|
| ## Benchmark |
|
|
| 97.8% clip accuracy on |
| [referee-lab/hands-visible](https://github.com/publu/referee-lab) (45-clip |
| provisional development sheet, robot + wildlife domains) — matching gemma3:27b |
| on that sheet at 1/18,000th the parameters. |
|
|
| | Model | Params | Accuracy | |
| |---|---|---| |
| | gemma3:27b | 27B | 97.8% | |
| | **referee-hands-s** | **1.5M** | **97.8%** | |
| | referee-hands-s, cross-video split | 1.5M | 95.6% | |
| | qwen2.5vl:32b | 32B | 74.4% | |
| | *constant-answer floor* | — | *68.9%* | |
|
|
| The cross-video row keeps every training frame at least 60 seconds away from |
| any test clip on its source stream. Under that separation the model scores |
| 100% on real footage (41/41); its two misses are synthetic drawn-hand control |
| clips outside the training distribution. |
|
|
| ## Intended use and scope |
|
|
| Version 1 targets fixed-camera and wrist-camera **robot workspace footage** — |
| the domain it was trained on (DROID, ALOHA-sim, KABR negatives). Human |
| egocentric and general webcam scenes are outside the v1 training distribution; |
| a mixed-domain v2 is in development. Use for capture-pipeline gating in the |
| supported domain. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from torchvision import models, transforms |
| from safetensors.torch import load_file |
| |
| net = models.mobilenet_v3_small() |
| net.classifier[3] = torch.nn.Linear(net.classifier[3].in_features, 1) |
| net.load_state_dict(load_file("model.safetensors")) |
| net.eval() |
| tf = transforms.Compose([transforms.Resize((96, 96)), transforms.ToTensor()]) |
| p = torch.sigmoid(net(tf(img).unsqueeze(0)).squeeze()) # P(hand/gripper visible) |
| ``` |
|
|
| Clip-level verdict: sample ~12 frames, yes if `max(p) > 0.5`. |
|
|
| Training: distilled from gemma3:27b pseudo-labels on CC BY / CC0 / MIT media. |
| Benchmark, gates, and tooling: https://github.com/publu/referee-lab |
|
|
