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README.md
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- π₯ For naming models, spaces, datasets, and metrics, employ [kebab-case](https://en.wiktionary.org/wiki/kebab_case). Use lowercase letters, except for acronyms, which may be capitalized.
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- π Please mind about making the models, spaces and datasets public or private. Metrics can (and have to) be public.
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- π€ Do not expose passwords or tokens, use [secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets).
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<details>
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<summary>
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Your metric should have the following structure:
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- Calls a metric engine, defined for example in the
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- Call the
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- All the metric's parameters, such as iou_threshold
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- Output should look like this:
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"ahoy_IR_b2_engine_3_6_0_49_gd81d3b63_oversea": {
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"overall": {
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"all": {
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</details>
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<h2> Looking for metrics? </h2>
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- https://huggingface.co/spaces/SEA-AI/det-metrics
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- Object detection metrics based on [`pycocotools`](https://github.com/cocodataset/cocoapi) and [torchmetrics' Mean Avergae Precision](https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html).
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- https://huggingface.co/spaces/SEA-AI/box-metrics
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- Bounding box statistics, including IOU, BEP (bottom edge proximity), and others.
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- https://huggingface.co/spaces/SEA-AI/horizon-metrics
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- Comparing horizons in an image w.r.t their midpoint and slope errors
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- https://huggingface.co/spaces/SEA-AI/mot-metrics
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- Multi-object-tracking metrics using [`py-motmetrics`](https://github.com/cheind/py-motmetrics)
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- https://huggingface.co/spaces/SEA-AI/panoptic-quality
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- Evaluating panoptic models
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</div>
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<div align="center">
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<h1> Want to contribute? </h1>
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</div>
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- π₯ For naming models, spaces, datasets, and metrics, employ [kebab-case](https://en.wiktionary.org/wiki/kebab_case). Use lowercase letters, except for acronyms, which may be capitalized.
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- π Please mind about making the models, spaces and datasets public or private. Metrics can (and have to) be public.
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- π€ Do not expose passwords or tokens, use [secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets).
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<div align="center">
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<h1> Looking for metrics? </h1>
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</div>
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- https://huggingface.co/spaces/SEA-AI/det-metrics
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- Object detection metrics based on [`pycocotools`](https://github.com/cocodataset/cocoapi) and [torchmetrics' Mean Avergae Precision](https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html).
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- https://huggingface.co/spaces/SEA-AI/box-metrics
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- Bounding box statistics, including IOU, BEP (bottom edge proximity), and others.
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- https://huggingface.co/spaces/SEA-AI/horizon-metrics
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- Comparing horizons in an image w.r.t their midpoint and slope errors
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- https://huggingface.co/spaces/SEA-AI/mot-metrics
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- Multi-object-tracking metrics using [`py-motmetrics`](https://github.com/cheind/py-motmetrics)
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- https://huggingface.co/spaces/SEA-AI/panoptic-quality
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- Evaluating panoptic models
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<details>
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<summary>Proposed Metric Output Structure</summary>
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Your metric should have the following structure:
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- `_compute(references, predictions)`
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- Calls a metric engine, defined for example in the `seametrics` package, or other
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- `compute_from_payload(paylaod)`
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- Call the `module.compute` method internally after converting payload -> references, predictions
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- All the metric's parameters, such as `iou_threshold`, `area_ranges`, etc.. should be moved to the `__init__` method.
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- Output should look like this:
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```json
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{
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"ahoy_IR_b2_engine_3_6_0_49_gd81d3b63_oversea": {
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"overall": {
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"all": {
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}
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}
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}
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}
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```
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</details>
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