This challenge is now closed. Thank you to all who participated.
If you are interested in future research, you can still request the dataset as we will be re-opening the evaluation system on the 15th September 2020.
Please request the dataset by emailing: email@example.com
F1-Score is the harmonic mean of Precision and Recall and gives a better measure of the incorrectly classified cases than the Accuracy Metric. For our task, F1-score is used as the False Negatives and False Positives are also crucial. False Positives will cause additional cost and burden to foot clinics, while False Negatives will risk further foot complications.
The second consideration is mAP, which is widely used to measure the overlap percentage of the prediction and ground truth, is commonly used in object detection metrics.
Participants will be ranked on these final metrics.
For each image in the test set, you must predict a list of boxes describing objects in the image. Each box is described as:
filename,xmin,ymin,xmax,ymax,score 100001.jpg,335,134,553,462,0.5 100001.jpg,598,306,663,302,0.551118 100002.jpg,519,355,655,432,0.641662 102000.jpg,401,295,477,265,0.962109 102000.jpg,428,386,546,435,0.403875 filename ....