#ActivityNet Large Scale Activity Recognition Challenge - Evaluation Toolkit This is the documentation of the ActivityNet Large Scale Activity Recognition Challenge Evaluation Toolkit. It includes APIs to evaluate the performance of a method in the two different tasks in the challenge: untrimmed video classification and activity detection. For more information about the challenge competitions, please read the guidelines.

##Dependencies The Evaluation Toolkit is purely written in Python (>=2.7) and it requires the following third party libraries:

• Numpy
• Pandas
• Joblib

##Getting started We include sample prediction files in the folder data to show how to evaluate your prediction results. Please follow this steps to obtain the performance evaluation on the provided sample files:

• Run git clone this repository.
• To evaluate classification performance call: python get_classification_performance.py data/activity_net.v1-3.min.json sample_classification_prediction.json
• To evaluate detection performance call: python get_detection_performance.py data/activity_net.v1-3.min.json sample_detection_prediction.json

##Contributions and Troubleshooting We are welcome to contributions, please keep your pull-request simple so we can go back to you as soon as we can. If you found a bug please open a new issue and describe the problem.