| --- |
| 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 |
| - sim-to-real |
| - tiny-cnn |
| model-index: |
| - name: Tiny Vial CNN SDG Baseline |
| results: |
| - task: |
| type: object-detection |
| name: Single-object bounding-box regression |
| dataset: |
| type: junbin666/isaac-blue-vial-sdg-contract |
| name: Isaac Blue Vial SDG Contract |
| split: test |
| metrics: |
| - type: iou |
| value: 0.893093 |
| name: Mean IoU |
| - type: accuracy |
| value: 1.0 |
| name: Success at IoU >= 0.5 |
| --- |
| |
| # Tiny Vial CNN SDG Baseline |
|
|
|  |
|
|
| A 141,292-parameter PyTorch CNN that regresses one `blue_vial` bounding box |
| from a 64×64 RGB image. It is the deliberately small baseline for the |
| [Isaac Sim-to-Real VLA Lab](https://huggingface.co/spaces/junbin666/isaac-sim2real-vla-lab) |
| and was trained on |
| [`junbin666/isaac-blue-vial-sdg-contract`](https://huggingface.co/datasets/junbin666/isaac-blue-vial-sdg-contract). |
|
|
| This is an **educational offline-contract baseline**, not a production object |
| detector or evidence of real-world robotics performance. |
|
|
| ## Results |
|
|
| | Evaluation | Result | |
| | --- | ---: | |
| | Best validation mean IoU | 0.869151 | |
| | Test mean IoU | 0.893093 | |
| | Test success at IoU ≥ 0.5 | 1.0 (9/9) | |
| | Parameters | 141,292 | |
|
|
| The test split contains only positive synthetic frames. These results do not |
| measure false positives, object-presence classification, real-camera |
| generalization, occlusion, multi-object detection, or Isaac Replicator imagery. |
|
|
| ## Files |
|
|
| - `model.safetensors`: recommended state dict |
| - `pytorch_model.pt`: original checkpoint loaded with `weights_only=True` |
| - `config.json`: architecture and preprocessing contract |
| - `inference.py`: standalone inference and PNG overlay example |
| - `eval_results.json`: per-frame held-out results |
| - `training_metrics.jsonl`: 20-epoch training history |
| - `release_report.json`: hashes and release verification |
|
|
| ## Inference |
|
|
| ```bash |
| pip install -r requirements.txt |
| python inference.py examples/test-frame-000006.png \ |
| --weights model.safetensors \ |
| --output prediction.png |
| ``` |
|
|
| Expected box for the included example: `[29, 20, 44, 47]`. |
|
|
| The model outputs normalized `(center_x, center_y, width, height)` and the |
| script converts it to pixel `[x_min, y_min, x_max, y_max]` coordinates. |
|
|
| ## Architecture and training |
|
|
| - Three stride-2 convolution layers: 3→12→24→32 channels |
| - ReLU activations and a two-layer bounding-box head |
| - Smooth L1 loss and Adam optimizer |
| - Input: RGB, resized to 64×64 and scaled to `[0, 1]` |
| - Seed: 7; CPU training; 20 epochs; batch size 16; learning rate 0.003 |
| - Training uses positive frames only. Dataset negatives are reserved for a |
| future objectness/classification head. |
|
|
| ## Intended use |
|
|
| - Reproduce a tiny synthetic-data bounding-box baseline |
| - Exercise checkpoint loading, evaluation, and inference contracts |
| - Demonstrate transparent mock/real boundaries in a robotics portfolio |
|
|
| ## Limitations |
|
|
| Do not use this checkpoint for safety-critical manipulation, production |
| robotics, or real-world claims. It assumes one visible blue vial and cannot |
| express “no object.” Its synthetic renderer and sample count are intentionally |
| small, and all held-out results are in-domain. |
|
|
| ## License |
|
|
| The model code and weights are released under the MIT License. The associated |
| dataset is CC BY 4.0. |
|
|
