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sourishg

Sourish Ghosh

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Scene Understanding for Autonomous Drone Delivery (SUADD'23)

AIcrowd

Amazon Prime Air

Understand semantic segmentation and monocular depth estimation from downward-facing drone images

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No submissions made in this challenge.

Completed

Airborne Object Tracking Challenge

Amazon Prime Air

Airborne Object Tracking Challenge

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failed	152916	Wed, 18 Aug 2021 03:59:36
graded	152096	Tue, 3 Aug 2021 04:20:30
graded	152030	Mon, 2 Aug 2021 00:36:10

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AirLab Airborne Object Tracking Challenge
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SUADD'23- Scene Understanding for Autonomous Drone

Error in local evaluation

Over 1 year ago

When I run the file local_evaluation.py, I get the following error:

Predicting Segmentation Masks: 100%|█████████████████████████████████████████████████████████████████████████████████████████| 1786/1786 [08:53<00:00,  3.35it/s]
Evaluating results:  74%|██████████████████████████████████████████████████████████████████████████▏                         | 1324/1786 [03:07<01:05,  7.06it/s]
Traceback (most recent call last):
  File "/Users/sourish/Projects/suadd-2023-semantic-segmentation-starter-kit/local_evaluation.py", line 100, in <module>
    evaluate(LocalEvalConfig)
  File "/Users/sourish/Projects/suadd-2023-semantic-segmentation-starter-kit/local_evaluation.py", line 76, in evaluate
    all_metrics[fname] = calculate_metrics(semantic_annotation, semantic_prediction)
  File "/Users/sourish/Projects/suadd-2023-semantic-segmentation-starter-kit/local_evaluation.py", line 38, in calculate_metrics
    mean_iou_score  = mean_iou(semantic_annotation, semantic_prediction)
  File "/Users/sourish/Projects/suadd-2023-semantic-segmentation-starter-kit/local_evaluation.py", line 28, in mean_iou
    numer = np.sum(class_annotation & class_prediction, axis=(0,1))
ValueError: operands could not be broadcast together with shapes (2250,1550) (2200,1550)

This is using the default random model.