[ICCV2019] Challenge - Computer Vision for Wildlife Conservation Solution
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Updated
Aug 5, 2019 - Python
[ICCV2019] Challenge - Computer Vision for Wildlife Conservation Solution
Pytorch implementation for "Iterative Human and Automated Identification of Wildlife Images" (Nature -Machine Intelligence, 2021)
Python library for downloading Animal tracking data from Anitra and Movebank platforms.
MegaDetector Desktop: Simple Interface for Detection of Humans, Animals and Vehicles in Camera Trap Imagery
📷🦔 CamTrapML Python Library for Detecting, Classifying, and Analysing Camera Trap Imagery.
CAMCALT (Complex Animal Movement Capture and Live Transmission) is a forest surveillance and monitoring system designed to capture complex animal movements and provide live video feed wirelessly from any part of the world. It aims to prevent hunting and poaching, enhancing forest security.
This repository contains scripts and resources to develop a GUI that helps biologist identify individual Archey's frogs (Leiopelma archeyi).
"Animal Behavior & Disease Detection: Utilize YOLO and MobileNetV2_img_classifier for real-time animal behavior tracking and disease identification. A valuable tool for wildlife researchers and conservationists. 🦁🔍🦠 #WildlifeAI #DeepLearning #Conservation"
This is repository containing a full pipeline (from annotation to training) for building an orangutans detector.
Sample implementation of the Anitra data API client.
Monitor endangered wildlife and assess potential threats using autonomous drones
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