https://github.com/aakaashjois/plant-tracer
A deep learning approach to tracking the apex of a moving plant
https://github.com/aakaashjois/plant-tracer
deep-learning neural-networks object-tracking opencv-python python pytorch
Last synced: about 2 months ago
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A deep learning approach to tracking the apex of a moving plant
- Host: GitHub
- URL: https://github.com/aakaashjois/plant-tracer
- Owner: aakaashjois
- License: apache-2.0
- Created: 2019-05-14T21:09:13.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-02-05T09:35:16.000Z (over 4 years ago)
- Last Synced: 2025-06-12T02:11:15.825Z (about 1 year ago)
- Topics: deep-learning, neural-networks, object-tracking, opencv-python, python, pytorch
- Language: Python
- Size: 29.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Plant Tracer
A deep learning approach to tracking the apex of a moving plant.
## Introduction
Plant Tracer is an app designed to enable analysis of plant movement from time-lapse videos. This repository contains a small part of the complete project. Here, I try to track the movement of the plant using a deep learning model to make it robust and work on videos with occlusion while tracking plants.
## Architecture
The architecture follows the architecture in [GOTURN](https://arxiv.org/pdf/1604.01802.pdf). The `CaffeNet` pretrained on `CIFAR-100` has been replaced with `AlexNet` pretrained on `ImageNet`. The architecture of the model is shown below.
## Instructions
1. Obtain data from [Plant Tracer homepage](https://www.planttracer.com/).
2. Clone this repository.
3. This project uses `conda` environment. Create the conda virtual environment using `conda env create -f environment.yaml`.
4. Modify the `run.py` file and run it to start the training procedure. This project uses [Comet](https://www.comet.ml) for all visualizations. Add your comet API key in the `run.py` file to see visualizations.
5. The models are saved in `models` folder and validation and testing results are stored in the `logs` directory and can be visualized with the `viz.py` file.
## Report
This repository contains the code used for submission of this [Report](./misc/Report.pdf).
## Result
The output from validation and testing of the model can be seen below.
#### Validation
#### Tracking Test
## License
Plant-Tracer is released under [Apache License 2.0](./LICENSE.md).
## Author
This repository and the approach has been created by [Aakaash Jois](https://aakaashjois.com).
The complete Plant Tracer application has multiple authors and details related to that can be found on [Plant Tracer homepage](https://www.planttracer.com).