MNHN-OFVI/DeepForestVision

Automated wildlife identification for camera traps of African tropical forests # DeepForestVision ## Foreword DeepForestVision is an AI model designed to identify wildlife on camera trap videos and images from African tropical forests. It is developed under CC BY-NC-SA 4.0 license (creativecommons.org) by an academic team from the French Muséum National d'Histoire Naturelle (MNHN) as part of the One Forest Vision initiative (oneforestvision.org). DeepForestVision is available in the AddaxAI interface (addaxdatascience.com) that can be run on Windows, Linux and MacOS without programming knowledge. This Github page provides the model weights and inference code. Contacts: hugo.magaldi@mnhn.fr; sabrina.krief@mnhn.fr ## Using DeepForestVision 1) Install the dependencies from requirements.text. If the *PytorchWildlife* library fails to install *boto3*, please install the dependencies *without* a virtual environment. 2) Run DFV.py to predict taxa from photos and videos using the following optional arguments: **--data_dir** (str, default = './data' ): Folder where your photos and videos to process are stored (can be a mix of both, accepts subfolders) **--predictions_dir** (str, default = './predictions'): Folder where you want the csv file with predictions to be stored (created automatically if non-existing) **--detection_threshold** (float, default = .2): Detection score threshold above which MegaDetector detections are kept (created automatically) **--stride** (float, default = 1): Number of seconds between two extracted frames for videos 3) Predictions are stored in csv format in the *predictions* folder. They contain, for each file (photo or video): file path, file name, scores of class, prediction, confidence score. ## Examples Standard use: ```python DFV.py``` With arguments: ```python DFV.py --detection_threshold .5 --stride .5 --data_dir '/home/documents/camera_trap_data' --predictions_dir '/home/documents/results'```