Step 1. Intersection over Union def intersection_over_union(dt_bbox, gt_bbox):  ---> return iou Step 2. Evaluate Sample  def evaluate_sample(target_pred, target_true, iou_threshold=0.5):           ---->          return results Step 3. Evaluate Model

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
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Step 1. Intersection over Union

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

Step 2. Evaluate Sample 

def evaluate_sample(target_pred, target_true, iou_threshold=0.5):           ---->          return results

Step 3. Evaluate Model

To assess the quality of the model, we will use the mAP metric defined as AP Area under the curve. To do this, you will need to calculate recall andprecision.

from sklearn.metrics import auc
 
def evaluate(model, test_loader, device):
  results = []
  model.eval()
  nbr_boxes = 0
  with torch.no_grad():
     for batch, (images, targets_true) inenumerate(test_loader):
     images = list(image.to(device).float() for image in images)
     targets_pred = model(images)

     targets_true = [{k: v.cpu().float() for k, v in t.items()} for t in targets_true]
     targets_pred = [{k: v.cpu().float() for k, v in t.items()} for t in      targets_pred]

     for i inrange(len(targets_true)):
        target_true = targets_true[i]
        target_pred = targets_pred[i]
        nbr_boxes += target_true['labels'].shape[0]

        results.extend(evaluate_sample(target_pred, target_true))

   results = sorted(results, key=lambda k: k['score'], reverse=True)

# compute precision and recall to calculate mAP
 
## YOUR CODE HERE
 
 
return auc(recall, precision)
 
# for refrence and data detail go to   ---> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb#scrollTo=jhmZOkQajpwZ
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