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Colab features #24

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Apr 15, 2024
Merged

Colab features #24

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f6e6594
append new feautures
MiXaiLL76 Apr 15, 2024
2319042
remove old eval
MiXaiLL76 Apr 15, 2024
1043b7a
append colab example
MiXaiLL76 Apr 15, 2024
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20 changes: 7 additions & 13 deletions README.md
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Expand Up @@ -24,22 +24,16 @@ for coco's AP metrics, especially when dealing with a high number of instances i
For our use case with a test dataset of 5000 images from the coco val dataset.
Testing was carried out using the mmdetection framework and the eval_metric.py script. The indicators are presented below.

Visualization of testing **comparison.ipynb** available in directory [examples/comparison](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/mmdet/comparison.ipynb)
Tested with yolo3 model (bbox eval) and yoloact model (segm eval)
Visualization of testing **colab_example.ipynb** available in directory [examples/comparison](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/mmdet/colab_example.ipynb)
[colab_example.ipynb in google collab](https://drive.google.com/file/d/13raC0nRsil797ITJSv3FTRvUvfEK9SD-/view?usp=sharing)
Tested with rtmdet model bbox + segm

### Summary for 5000 imgs

| Type | COCOeval | COCOeval_faster | Profit |
| ---- | ----------- | --------------- | ------------ |
| bbox | 18.477 sec. | 7.345 sec. | ~2.5x faster |
| segm | 29.819 sec. | 15.840 sec. | ~1.7x faster |

## Summary for 500 imgs

| Type | COCOeval | COCOeval_faster | Profit |
| ---- | --------- | --------------- | ------------ |
| bbox | 3.57 sec. | 2.03 sec. | ~1.7x faster |
| segm | 4.16 sec. | 2.41 sec. | ~1.7x faster |
| Type | faster-coco-eval | pycocotools | Profit |
| :--- | ---------------: | ----------: | -----: |
| bbox | 5.812 | 22.72 | 3.909 |
| segm | 7.413 | 24.434 | 3.296 |

## Feautures

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