Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes = target_pred['boxes'].numpy() dt_labels = target_pred['labels'].numpy() dt_scores = target_pred['scores'].numpy() results = [] # for each bounding box from the prediction, find the closest bounding box among the answers for detection_id inrange(len(dt_labels)): dt_bbox = dt_bboxes[detection_id, :] dt_label = dt_labels[detection_id] dt_score = dt_scores[detection_id] detection_result_dict = {'score': dt_score} ## YOUR CODE HERE if max_gt_id >= 0and max_IoU >= iou_threshold: # mark as True Positive detection_result_dict['TP'] = 1 # delete matched bounding box gt_labels = np.delete(gt_labels, max_gt_id, axis=0) gt_bboxes = np.delete(gt_bboxes, max_gt_id, axis=0) else: detection_result_dict['TP'] = 0 results.append(detection_result_dict) return results #for refrence and data detail go to link --> https://colab.research.google.com/github/hse-aml/intro-t

Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes = target_pred['boxes'].numpy() dt_labels = target_pred['labels'].numpy() dt_scores = target_pred['scores'].numpy() results = [] # for each bounding box from the prediction, find the closest bounding box among the answers for detection_id inrange(len(dt_labels)): dt_bbox = dt_bboxes[detection_id, :] dt_label = dt_labels[detection_id] dt_score = dt_scores[detection_id] detection_result_dict = {'score': dt_score} ## YOUR CODE HERE if max_gt_id >= 0and max_IoU >= iou_threshold: # mark as True Positive detection_result_dict['TP'] = 1 # delete matched bounding box gt_labels = np.delete(gt_labels, max_gt_id, axis=0) gt_bboxes = np.delete(gt_bboxes, max_gt_id, axis=0) else: detection_result_dict['TP'] = 0 results.append(detection_result_dict) return results #for refrence and data detail go to link --> https://colab.research.google.com/github/hse-aml/intro-t

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Step 1. Intersection over Union

# def intersection_over_union(dt_bbox, gt_bbox): ---> return iou

Step 2. Evaluate Sample

We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following:

Take model predictions and ground truth bounding boxes and labels as inputs.
For each bounding box from the prediction, find the closest bounding box among the answers.
For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive.
Remove a matched bounding box from the evaluation.
For each predicted bounding box, return the detection score and whether we were able to match it or not.

def evaluate_sample(target_pred, target_true, iou_threshold=0.5):

# ground truth

gt_bboxes = target_true['boxes'].numpy()

gt_labels = target_true['labels'].numpy()

# predictions

dt_bboxes = target_pred['boxes'].numpy()

dt_labels = target_pred['labels'].numpy()

dt_scores = target_pred['scores'].numpy()

results = []

# for each bounding box from the prediction, find the closest bounding box among the answers

for detection_id inrange(len(dt_labels)):

dt_bbox = dt_bboxes[detection_id, :]

dt_label = dt_labels[detection_id]

dt_score = dt_scores[detection_id]

detection_result_dict = {'score': dt_score}

## YOUR CODE HERE

if max_gt_id >= 0and max_IoU >= iou_threshold:

# mark as True Positive

detection_result_dict['TP'] = 1

# delete matched bounding box

gt_labels = np.delete(gt_labels, max_gt_id, axis=0)

gt_bboxes = np.delete(gt_bboxes, max_gt_id, axis=0)

else:

detection_result_dict['TP'] = 0

results.append(detection_result_dict)

return results

#for refrence and data detail go to link --> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb#scrollTo=Loadw2Krkq_a

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