AIcrowd | sourishg | Participants

0 Follower

0 Following

sourishg

Sourish Ghosh

Activity

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mon

Wed

Fri

Ratings Progression

Challenge Categories

Challenges Entered

Completed

Scene Understanding for Autonomous Drone Delivery (SUADD'23)

AIcrowd

Amazon Prime Air

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

Latest submissions

No submissions made in this challenge.

Completed

Airborne Object Tracking Challenge

Amazon Prime Air

Airborne Object Tracking Challenge

Latest submissions

See All

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

Participant	Rating

Participant	Rating

AirLab Airborne Object Tracking Challenge
View

SUADD'23- Scene Understanding for Autonomous Drone

Error in local evaluation

Almost 2 years 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.