---
title: README
emoji: 👀
colorFrom: gray
colorTo: gray
sdk: static
pinned: false
---


<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6567e9af88bfbc261a34673d/riHfcszflP6BzNls2Phrf.png" 
    width=400px
    style="border-radius: 20px;"
    />
  
</div>

<div align="center">
  <h2> Want to contribute? </h2>
</div>

- 🥙 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.
- 🔒 Please mind about making the models, spaces and datasets public or private. Metrics can (and have to) be public.
- 🤐 Do not expose passwords or tokens, use [secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets).

<details>
  <summary>Structure. (Click to expand)</summary>

Your metric should have the following structure:
- ```_compute(references, predictions)```
  - Calls a metric engine, defined for example in the ```seametrics``` package, or other
- ```compute_from_payload(paylaod)```
  - Call the ```module.compute``` method internally after converting payload -> references, predictions
- All the metric's parameters, such as iou_threshold, area_ranges, etc.. should be moved to the ```__init__``` method.
- Output should look like this:
    ```json
    {
    "ahoy_IR_b2_engine_3_6_0_49_gd81d3b63_oversea": {
        "overall": {
            "all": {
                "f1": 0.15967351103175614,
                "fn": 2923.0,
                "fp": 3666.0,
                "num_gt_ids": 10,
                "precision": 0.14585274930102515,
                "recall": 0.1763877148492533,
                "recognition_0.3": 0.1,
                "recognition_0.5": 0.1,
                "recognition_0.8": 0.1,
                "recognized_0.3": 1,
                "recognized_0.5": 1,
                "recognized_0.8": 1,
                "tp": 626.0
            }
        },
        "per_sequence": {
            "Sentry_2023_02_08_PROACT_CELADON_@6m_MOB_2023_02_08_12_51_49": {
                "all": {
                    "f1": 0.15967351103175614,
                    "fn": 2923.0,
                    "fp": 3666.0,
                    "num_gt_ids": 10,
                    "precision": 0.14585274930102515,
                    "recall": 0.1763877148492533,
                    "recognition_0.3": 0.1,
                    "recognition_0.5": 0.1,
                    "recognition_0.8": 0.1,
                    "recognized_0.3": 1,
                    "recognized_0.5": 1,
                    "recognized_0.8": 1,
                    "tp": 626.0
                }
            }
        }
    }
}```

</details>

<div align="center">
  <h2> Looking for metrics? </h2>
</div>

- https://huggingface.co/spaces/SEA-AI/det-metrics
  - 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).
- https://huggingface.co/spaces/SEA-AI/box-metrics
  - Bounding box statistics, including IOU, BEP (bottom edge proximity), and others.
- https://huggingface.co/spaces/SEA-AI/horizon-metrics
  - Comparing horizons in an image w.r.t their midpoint and slope errors
- https://huggingface.co/spaces/SEA-AI/mot-metrics
  - Multi-object-tracking metrics using [`py-motmetrics`](https://github.com/cheind/py-motmetrics) 
- https://huggingface.co/spaces/SEA-AI/panoptic-quality
  - Evaluating panoptic models