| --- |
| license: cc-by-nc-4.0 |
| extra_gated_heading: Acknowledge the terms to access this dataset |
| extra_gated_prompt: >- |
| Access is granted for non-commercial research and evaluation use only. By requesting access you |
| agree not to redistribute this dataset or any derivative data, to cite Dhi Technologies in any |
| publication or output that uses it, and to obtain a separate commercial license via dhi-tech.com |
| before any commercial use. Access requests are reviewed manually by Dhi Technologies. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| Intended use: text |
| I agree to use this dataset for non-commercial research and evaluation only, and not to redistribute it: checkbox |
| extra_gated_button_content: Submit request |
| task_categories: |
| - image-classification |
| - object-detection |
| tags: |
| - vision |
| - automl |
| - onnx |
| - synthetic |
| pretty_name: Intent2Edge Toy Examples |
| size_categories: |
| - n<1K |
| --- |
| |
| # Intent2Edge Toy Examples |
|
|
| **Product:** [Intent2Edge](https://github.com/DHI-Technologies-Inc/intent2edge), a compiler that turns a natural-language edge-vision request into a trained ONNX candidate with a recomputable proof bundle. The pipeline runs a typed sequence: prompt, dataset config, training, calibration with conformal abstention, ONNX export, an optional compression step with an accuracy-floor gate, and a hard-case capture store. |
|
|
| ## What this is (and isn't) |
|
|
| This is **not a benchmark dataset**. It holds the small synthetic fixtures that exercise the pipeline end to end, generated by the code repository's toy-data command, plus one pipeline output file for those fixtures. The method is evaluated in paper 03 on five public image-classification datasets, and the measured results are summarized below. |
|
|
| The fixtures let a reader check the pipeline's inputs and its output format against the actual files. |
|
|
| ## What's in this dataset |
|
|
| - **`classification/{red_square,green_triangle,blue_circle}/`**: 12 procedurally drawn 128x128 PNGs per class (36 images). |
| - **`detection/images/`**: 12 PNGs, `image_001.png` through `image_012.png`, 128x128 each, plus **`detection/annotations.json`**, a standard-format toy detection set with 2 categories (`square`, `circle`) and 24 bounding boxes (2 per image). |
| - **`smoke_test_results.json`**: the pipeline's output file for these fixtures (training, calibration, ONNX export and runtime check). It is included as a format example. Its values come from a toy-scale run and are not reported as results anywhere in this repository. |
| |
| ## How to load it |
| |
| ```python |
| import json |
| from huggingface_hub import hf_hub_download |
| |
| repo_id = "Dhi-Technologies/intent2edge-examples" |
| ann_path = hf_hub_download(repo_id, "detection/annotations.json", repo_type="dataset") |
| smoke_path = hf_hub_download(repo_id, "smoke_test_results.json", repo_type="dataset") |
| |
| annotations = json.load(open(ann_path)) |
| smoke = json.load(open(smoke_path)) |
| |
| print(len(annotations["images"]), "images,", len(annotations["annotations"]), "boxes") |
| print(smoke["classification"]["metrics"]["calibration"].keys()) |
| ``` |
| |
| To fetch a classification image, use the same call, for example `hf_hub_download(repo_id, "classification/red_square/red_square_000.png", repo_type="dataset")`. |
| |
| ## Results |
| |
| The measured results of Intent2Edge are in paper 03, *Intent2Edge: Compiling Natural-Language Intent into Refuse-Closed Edge Vision Artifacts* (manuscript in preparation). Headline results: |
| |
| - **Prompt-guided versus fixed augmentation.** Pooled over five stated deployment conditions, the guided arm scores 5.9 points above the fixed arm on matched corruption families (bootstrap 95% interval +4.6 to +7.3; 15 dataset-seed pairs). Guided is higher in all five conditions. |
| - **Validity gate.** On 742 trained configurations, a confidence-calibrated gate at confidence 0.7 lowers false accepts on the seed-23 configurations from 30 to 14, at 1.22 times the refusals. |
| - **Export fidelity and retention.** The FP32 ONNX export reproduces PyTorch logits to a maximum absolute difference of 2.0e-04. On a frozen independent split, 10 of 10 QDQ ONNX graphs keep at least 0.98 of their FP32 accuracy. |
|
|
| The fixtures in this repository are not used for those results. |
|
|
| ## Reproducing the pipeline run on these fixtures |
|
|
| ``` |
| PYTHONPATH=src python -m prompt2model.cli generate-toy-data --task all --output-dir output/toy_data |
| PYTHONPATH=src python -m prompt2model.cli smoke-test --output-dir output/smoke |
| ``` |
|
|
| The module name is as it appears in the code repository at the commit used for this release. |
|
|
| ## Pipeline behavior that the fixtures exercise |
|
|
| - The pipeline seeds `torch` and `random` from `config.dataset.seed` (default 42). |
| - The train, validation and test split is stratified by class, so every class is present in each split. |
| - The compression step keeps the uncompressed model unless the compressed model holds the accuracy floor (relative floor 0.98 by default). |
|
|
| ## Limitations |
|
|
| - Toy scale only: 36 classification images and 12 detection images. These fixtures check that the pipeline runs end to end. They do not measure model quality. |
| - The fixtures contain no held-out real-world evaluation set. |
|
|
| ## License |
|
|
| This dataset (fixtures and pipeline output) is released under **CC BY-NC 4.0** (non-commercial). Access is gated and requires manual approval. It is provided for non-commercial research and evaluation only. Redistribution is not permitted, and any publication or output using it should cite Dhi Technologies. Commercial use requires a separate agreement; contact [dhi-tech.com](https://dhi-tech.com). The code repository linked above is licensed separately under the PolyForm Noncommercial License 1.0.0 (commercial use: https://dhi-tech.com/pricing/). |
|
|
| ## Try it |
|
|
| - Demo: [intent2edge-demo](https://huggingface.co/spaces/Dhi-Technologies/intent2edge-demo) |
| - Model: [intent2edge-reference-onnx](https://huggingface.co/Dhi-Technologies/intent2edge-reference-onnx) |
| - Blog: [Six research systems, one honesty thesis](https://huggingface.co/datasets/Dhi-Technologies/blog/blob/main/04_portfolio_overview.md) |
| - Dhi Labs overview: [dhi-tech.com/labs](https://dhi-tech.com/labs) |
| - Dhi Labs product page: [dhi-tech.com/labs/prompt2model/](https://dhi-tech.com/labs/prompt2model/) |
|
|
| ## Source and research context |
|
|
| - Code (public, PolyForm Noncommercial 1.0.0): https://github.com/DHI-Technologies-Inc/intent2edge |
| - Companion paper: Dhi Labs paper 03 (Intent2Edge), manuscript in preparation |
| - Collection: [Dhi Labs, honest edge vision AI](https://huggingface.co/collections/Dhi-Technologies/dhi-labs-honest-edge-vision-ai-6a4eb297cbd60f5f673cc2d7) |
| - Blog dataset: https://huggingface.co/datasets/Dhi-Technologies/blog |
| - Org: https://huggingface.co/Dhi-Technologies, GitHub org: https://github.com/DHI-Technologies-Inc |
|
|
| ## Commercial licensing |
|
|
| Research and evaluation use is free. Production and commercial use is licensed self-serve with published prices. |
|
|
| - Get a license: https://dhi-tech.com/buy/prompt2model |
| - All published prices: https://dhi-tech.com/pricing/ |
|
|
