https://github.com/mrdvince/anga

Using a pre-trained efficientnet (for experimental purposes) to classifier plant diseases given an image. Plant village challenge dataset.
https://github.com/mrdvince/anga

deep-learning efficientnet efficientnet-pytorch mixnet mixnet-pytorch plant-disease pytorch transfer-learning

Last synced: 6 months ago
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Using a pre-trained efficientnet (for experimental purposes) to classifier plant diseases given an image. Plant village challenge dataset.

• Host: GitHub
• URL: https://github.com/mrdvince/anga
• Owner: mrdvince
• License: gpl-3.0
• Created: 2019-08-04T02:34:49.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-05-01T22:23:54.000Z (almost 5 years ago)
• Last Synced: 2025-03-29T04:48:16.679Z (11 months ago)
• Topics: deep-learning, efficientnet, efficientnet-pytorch, mixnet, mixnet-pytorch, plant-disease, pytorch, transfer-learning
• Language: Python
• Homepage:
• Size: 186 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
![png](screenshots/leaf_imgs.png)

# Plant Disease Classifier

Using a pre-trained efficientnet (`for experimental purposes`) to classifier plant diseases given an image.

The efficient net paper can be found [here](https://arxiv.org/abs/1905.11946) and the pretrained pytorch model got from torch hub 

using ```torch.hub.list('rwightman/gen-efficientnet-pytorch')```

> For more cool PyTorch pre-trained models, check out Ross Wightman [pytorch models repo](https://github.com/rwightman/pytorch-image-models)

Download the dataset from the following link: https://www.crowdai.org/challenges/plantvillage-disease-classification-challenge

# Getting Started

## Dependencies

To set up your python environment to run the code in this repository, follow the instructions below.

1. Create (and activate) a new environment with Python 3.6.

	- __Linux__ or __Mac__: 

	```bash

	conda create --name py39 python=3.9.2

	conda activate py39

	```

alternatively, use virtual environments if you don't have Anaconda installed.

2. Clone the repository (if you haven't already!), and navigate to the `anga` folder.  Then, install several dependencies.

```bash

git clone https://github.com/mrdvince/anga

cd anga

```

## FastAPI endpoint

A minimal API endpoint to expose your model, you can make it more robust if you want.

Run ```uvicorn api.main:app``` and visit you ```local ip``` if running locally port ```8000``` playground to access the interactive docs (included by default). i.e. `http://127.0.0.1:8000/playground`

![png](screenshots/fastapi.png)

## Training

See the README on this [link](https://github.com/mrdvince/pytorchtemplate) has been([forked from](https://github.com/victoresque/pytorch-template)). The readme contains information on the folder structures and how to modify the hyperparameters to your liking.

On a high level:

- the config json file contains the model hyperparamaters and other settings

Run `python train.py -c config.json` to train the model.

## Metrics

Logged using tensorboard

### Train and Validation Accuracies 

![png](screenshots/acc.png)

### Train and Validation Loss 

![png](screenshots/loss.png)

## Inputs

![png](screenshots/input.png)