https://github.com/Evolving-AI-Lab/deep_learning_for_camera_trap_images
This repo contains code+pre-trained models for extracting information from camera-trap images. The pre-trained models have been trained on the Snapshot Serengeti dataset.
https://github.com/Evolving-AI-Lab/deep_learning_for_camera_trap_images
camera-traps deep-learning
Last synced: about 1 year ago
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This repo contains code+pre-trained models for extracting information from camera-trap images. The pre-trained models have been trained on the Snapshot Serengeti dataset.
- Host: GitHub
- URL: https://github.com/Evolving-AI-Lab/deep_learning_for_camera_trap_images
- Owner: Evolving-AI-Lab
- License: mit
- Created: 2018-03-08T01:02:33.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-07-21T12:32:29.000Z (almost 3 years ago)
- Last Synced: 2025-04-10T17:34:21.828Z (about 1 year ago)
- Topics: camera-traps, deep-learning
- Language: Python
- Homepage:
- Size: 40 KB
- Stars: 117
- Watchers: 15
- Forks: 52
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# deep_learning_for_camera_trap_images
This repository contains the code used for the following paper:
[Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning](http://www.pnas.org/content/early/2018/06/04/1719367115)
Authors: [Mohammad Sadegh Norouzzadeh](http://arash.norouzzadeh.com), Anh Nguyen, Margaret Kosmala, Ali Swanson, Meredith Palmer, Craig Packer, Jeff Clune
**If you use this code in an academic article, please cite the following paper:**
```
@article {Norouzzadeh201719367,
author = {Norouzzadeh, Mohammad Sadegh and Nguyen, Anh and Kosmala, Margaret and Swanson, Alexandra and Palmer, Meredith S. and Packer, Craig and Clune, Jeff},
title = {Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning},
year = {2018},
doi = {10.1073/pnas.1719367115},
publisher = {National Academy of Sciences},
issn = {0027-8424},
URL = {http://www.pnas.org/content/early/2018/06/04/1719367115},
eprint = {http://www.pnas.org/content/early/2018/06/04/1719367115.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}
```
Most of the code in this repository is taken from [here](https://github.com/arashno/tensorflow_multigpu_imagenet)
This repository has four independent parts:
1- The code used for Task I: Detecting Images That Contain Animals (phase1 folder)
2- The code used for Task II,III, and IV: identifying, counting, and describing animals in images (phase 2 folder)
3- The code used for Task II only, (all the transfer learning experiments for Task II used this part of the repo) (phase2_recognition_only folder)
4- resize.py is used for resizing the input images for all the other parts
For more information on how to use this repo please refer to the base repo at [this link](https://github.com/arashno/tensorflow_multigpu_imagenet)
## 1. Requirements
### Requirements
To use this code, you will need to install the following:
* Python 2.7
* Tenorflow
* NumPy
* SciPy
* MatPlot Lib
### 2. Running
Pre-trained models could be found at the following links:
* Phase 1 (VGG architecture):
https://drive.google.com/file/d/1Y-aDWNMfvgYUb-u-_cqzibZ6ePFOOLGj/view?usp=sharing
* Phase 2 (ResNet-152 architecture):
https://drive.google.com/file/d/1KTV9dmqkv0xrheIOEkPXbqeg36_rXJ_E/view?usp=sharing
* Phase 2 recognition only (ResNet-152 architecture):
https://drive.google.com/file/d/1cAcnyBTO5JeB2zSaEoGBWf0Jd-jAnguS/view?usp=sharing
## 3. Licenses
This code is licensed under MIT License.
## 4. Questions?
For questions/suggestions, feel free to [email](mailto:arash.norouzzadeh@gmail.com), tweet to [@arashnorouzzade](https://twitter.com/arashnorouzzade) or create a github issue.