ERROR:root:An error occurred: index 4 is out of bounds for dimension 1 with size 2. In the case of confusion matrix calculation, if the model does not predict any bounding box in an image, Does the code handles this case?

[HassanBinHaroon]

In the case of confusion matrix calculation, if the model does not predict any bounding box in an image, Does the code handles this case?

The Confusion Matrix Computation Code:

"def process_batch(self, detections, labels): """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: detections (Array[N, 6]), x1, y1, x2, y2, conf, class labels (Array[M, 5]), class, x1, y1, x2, y2 Returns: None, updates confusion matrix accordingly """ detections = detections[detections[:, 4] > self.conf] gt_classes = labels[:, 0].int() detection_classes = detections[:, 5].int() iou = general.box_iou(labels[:, 1:], detections[:, :4])

x = torch.where(iou > self.iou_thres) if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] else: matches = np.zeros((0, 3))

n = matches.shape[0] > 0 m0, m1, _ = matches.transpose().astype(np.int16) for i, gc in enumerate(gt_classes): j = m0 == i if n and sum(j) == 1: self.matrix[gc, detection_classes[m1[j]]] += 1 # correct else: self.matrix[self.nc, gc] += 1 # background FP

if n: for i, dc in enumerate(detection_classes): if not any(m1 == i): self.matrix[dc, self.nc] += 1 # background FN"