Requirements:

- python > 3.5
- pandas
- numpy
- sklearn
- matplotlib
- Tensorflow GPU

Installation:

$ pip install libmr, pandas, numpy, sklearn, matplotlib, seaborn, hyperopt

Installing Tensorflow-GPU

The best method to install tensorflow-gpu in your conda environment is to use conda install -c anaconda tensorflow-gpu.

$ conda install -c anaconda tensorflow-gpu

Usage:

- "train_model.py" :
	Used to train modified pre-trained models with the desired dataset. 
	The Dataset should contain ***class_names*** for *sub-directories* inside the main directory.
	The dataset using in this work is sample of ***Snapshot Serengeti Dataset***.
	
- "Serengeti_GUI.py" 
	contains code for loading the trained models and to initialize GPU for feeding images to model and an output window to view results.
	This GUI is designed for using with wildlife data.

Screenshots:

These are the screenshots of GUI designed for Automatic Classification of Camera Trap Images

For Using Snapshot Serengeti Dataset:

The dataset can be downloaded from here

This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the Snapshot Serengeti project, the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs.

Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available here.

For testing models trained with Snapshot Serengeti Dataset, a set of random images collected from the internet is provided in the folder OutSampleImages.

Attribution:

The images and species-level labels are described in more detail in the associated manuscript:

Swanson AB, Kosmala M, Lintott CJ, Simpson RJ, Smith A, Packer C (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific Data 2: 150026.