Plant Seedlings Classification with Deep Learning Studio
Can you differentiate a weed from a crop seedling?
The ability to do so effectively can mean better crop yields and better stewardship of the environment.
The Aarhus University Signal Processing group, in collaboration with University of Southern Denmark, has recently released a dataset containing images of approximately 960 unique plants belonging to 12 species at several growth stages.
You are provided with a training set and a test set of images of plant seedlings at various stages of grown. Each image has a filename that is its unique id. The dataset comprises 12 plant species. The goal of the article is to create a classifier capable of determining a plant’s species from a photo. The list of species is as follows:
In this article I will build a simple neural network to categorize given input into twelve classes, using Deep Learning Studio
If you are not familiar with how to use Deep Learning Studio take a look at this :)
About Deep Learning Studiotowardsdatascience.com
Is Deep Learning without Programming Possible?towardsdatascience.com
A video walkthrough of Deep Cognition by Favio Vázquez
Hi everyone! In this article I’ll share with you several videos that will walk you through Deep Cognition’s Platform…towardsdatascience.com
“Information: The Custom Dataset which I prepared according to Deep Learning Studio is available here so all of you can download the dataset from there and for the trained model I used you can download from my GitHub repository”
Now we will see how to build this model step by step
1) Project Creation:
After you log in to Deep Learning Studio that is either running locally or in cloud click on + button to create a new project.
2) Upload dataset:
Download the dataset from here .
2) You need to upload custom dataset and for that
2.1) Go to my Datasets
2.2) Click on the “upload dataset” option
2.3) After selecting the zip file simply upload dataset file.
3) Dataset Intake:
We then setup dataset for this project in “Data” tab. Usually 80% — 20% is a good split between training and validation but you can use other setting if you prefer. Also don’t forget to set Load Dataset in Memory to “Full dataset” if your machine has enough RAM to load full dataset in RAM.
4) Create the Neural Network
You can create a neural network as shown below by dragging and dropping the layers.
Configuration of the network:
5) Hyperparameter and Training:
Hyperparameters that I have used are shown below. Feel free to change and experiment with them.
Finally, you can start the training from Training Tab and monitor the progress with training dashboard.
Once you complete your training you can check the results in results tab. I have achieved around 86% accuracy on the Validation dataset.
With Deep Learning Studio you can easily check the inference on validation and test dataset at different layers of the network.
So, the main purpose of this article is to build a simple deep learning model using Deep Learning Studio.