Extracting mAP for each class

[HeinZingerRyo]

Does anybody know how to extract mAP for each class during evaluation? thx

[HeinZingerRyo]

Fixed. One may follow this link

More specifically, one possible modification is:

in engine.py:

# accumulate predictions from all images
    if coco_evaluator is not None:
        coco_evaluator.accumulate()
        coco_evaluator.summarize()

Add these lines after coco_evaluator.summarize() and afterwise the code is like

    # accumulate predictions from all images
    if coco_evaluator is not None:
        coco_evaluator.accumulate()
        coco_evaluator.summarize()

        classwise=True
        if classwise:
            # Compute per-category AP
            # from https://github.com/facebookresearch/detectron2/blob/03064eb5bafe4a3e5750cc7a16672daf5afe8435/detectron2/evaluation/coco_evaluation.py#L259-L283 # noqa

            cocoEval = coco_evaluator.coco_eval['bbox']
            coco = coco_evaluator.coco_eval['bbox'].cocoDt

            precisions = cocoEval.eval['precision']
            catIds = coco.getCatIds()
            # precision has dims (iou, recall, cls, area range, max dets)
            assert len(catIds) == precisions.shape[2]

            results_per_category = []
            for idx, catId in enumerate(catIds):
                # area range index 0: all area ranges
                # max dets index -1: typically 100 per image
                nm = coco.loadCats(catId)[0]
                precision = precisions[:, :, idx, 0, -1]
                precision = precision[precision > -1]
                ap = np.mean(precision) if precision.size else float('nan')
                results_per_category.append(
                    ('{}'.format(nm['name']),
                     '{:0.3f}'.format(float(ap * 100))))

            N_COLS = min(6, len(results_per_category) * 2)
            results_flatten = list(itertools.chain(*results_per_category))
            headers = ['category', 'AP'] * (N_COLS // 2)
            results_2d = itertools.zip_longest(
                *[results_flatten[i::N_COLS] for i in range(N_COLS)])
            table_data = [headers]
            table_data += [result for result in results_2d]
            table = AsciiTable(table_data)
            print(table.table)