| --- |
| language: |
| - en |
| license: mit |
| library_name: pytorch |
| pipeline_tag: object-detection |
| datasets: |
| - junbin666/isaac-blue-vial-sdg-contract |
| tags: |
| - nvidia |
| - isaac-sim |
| - synthetic-data |
| - robotics |
| - objectness |
| - tiny-cnn |
| model-index: |
| - name: Tiny Vial CNN SDG Objectness v2 |
| results: |
| - task: |
| type: object-detection |
| name: Object presence and single-object localization |
| dataset: |
| type: junbin666/isaac-blue-vial-sdg-contract |
| name: Isaac Blue Vial SDG Contract |
| split: model-specific test holdout |
| metrics: |
| - type: precision |
| value: 1.0 |
| name: Objectness precision |
| - type: recall |
| value: 1.0 |
| name: Objectness recall |
| - type: iou |
| value: 0.885074 |
| name: Positive mean IoU |
| --- |
| |
| # Tiny Vial CNN SDG Objectness v2 |
|
|
| Tiny CNN v2 adds an explicit objectness head to the v1 bounding-box baseline. |
| It can return “no object” for negative synthetic frames before producing a |
| single `blue_vial` box. It supports the public |
| [Isaac Sim-to-Real VLA Lab](https://huggingface.co/spaces/junbin666/isaac-sim2real-vla-lab) |
| and uses |
| [`junbin666/isaac-blue-vial-sdg-contract`](https://huggingface.co/datasets/junbin666/isaac-blue-vial-sdg-contract). |
|
|
| This is an educational synthetic-data checkpoint, not a production detector or |
| safety claim. |
|
|
| ## Test results |
|
|
| | Metric | Result | |
| | --- | ---: | |
| | Precision | 1.000 | |
| | Recall | 1.000 | |
| | False-positive rate | 0.000 | |
| | Observed false positives | 0 / 100 negatives | |
| | FPR upper bound (95%, rule of three) | 0.030 | |
| | Accuracy | 1.000 | |
| | Positive mean IoU | 0.885074 | |
| | IoU ≥ 0.5 on positive frames | 1.000 | |
|
|
| The model-specific test holdout contains **109 frames: 9 positive and 100 |
| synthetic hard negatives**. Its observed FPR is 0/100. With no observed |
| failures, the rule-of-three gives an approximate one-sided 95% FPR upper bound |
| of **3%**; this is more informative than the earlier two-negative check, but it |
| is not evidence of real-camera robustness. |
|
|
| The public dataset repository keeps its original 81/10/9 split. For this v2 |
| release, a separate deterministic 300-frame synthetic hard-negative suite is |
| divided 180/20/100 across train/validation/test. Its eight challenge families |
| include empty scenes, distractor-colored vials, blue geometric objects, |
| occlusion, and multiple blue boxes. Positive frames retain their original |
| source splits, and the 100 test negatives are never used for fitting or |
| threshold selection. |
|
|
| ## Architecture |
|
|
| - 64×64 RGB input |
| - Three stride-2 convolution layers: 3→12→24→32 |
| - Shared 64-unit representation |
| - One objectness logit plus four sigmoid box values |
| - BCE-with-logits objectness loss |
| - Smooth L1 box loss, applied only to positive samples |
| - 141,357 parameters |
|
|
| ## Files |
|
|
| - `model.safetensors`: recommended state dictionary |
| - `pytorch_model.pt`: original checkpoint, load with `weights_only=True` |
| - `config.json`: preprocessing, threshold, and output contract |
| - `inference.py`: standalone positive/negative inference |
| - `eval_results.json`: confusion counts and per-frame held-out results |
| - `training_metrics.jsonl`: deterministic training history |
| - `release_report.json`: hashes and package verification |
|
|
| ## Inference |
|
|
| ```bash |
| pip install -r requirements.txt |
| python inference.py examples/positive-000006.png --weights model.safetensors --output prediction.png |
| python inference.py examples/negative-000200.png --weights model.safetensors --output negative.png |
| ``` |
|
|
| The JSON output includes `detected`, `objectness_score`, `threshold`, and an |
| optional pixel-space `bbox`. |
|
|
| ## Limitations |
|
|
| The training and evaluation images come from small procedural renderers. The |
| checkpoint has not been tested on a real camera, Isaac Replicator output, |
| unfamiliar real containers, or safety-critical robot motion. The expanded |
| hard-negative suite exercises synthetic occlusion and multi-object distractors, |
| but cannot measure real-world domain shift. A robust release still needs real |
| camera evaluation, broader failure-mode coverage, and calibrated uncertainty. |
|
|
| ## License |
|
|
| Model code and weights use the MIT License. The associated dataset uses CC BY |
| 4.0. |
|
|
