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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

Can you detect plant diseases just by examining the images of the plant’s leaves? Sounds Tough. Can you do the same task if the images are at a really low resolution? Welcome to your new challenge!

In this task participants have to correctly identify a disease-class from low-resolution images of plant-leaves

Understand with code! Here is some code to get you started right away! 😄

## 💾 Dataset

The dataset consists of 43525 images of plant leaves at 32x32 pixels. Each of the images belongs to one of the 38 disease classes (some of these classes also represent healthy leaves). A separate test dataset of 10779 images is provided, and the task is to predict the associated disease (from the 38 valid disease classes) for each of the images. The training and the test set reflect numerous real-world properties of datasets such as class imbalance (some classes have about ~4500 images, while some classes can have as low as ~120 images in the training set) etc.

The images for both the training and the test set are aggregated into individual Numpy array files for convenient programmatic access.

## 📁 Files

Following files are available in the resources section:

• train-images.npy - (43466 samples) A numpy file with an ndarray of shape (43525, 32, 32, 3) representing all the images in the training set.
• train-labels.npy - (43466 samples) A numpy file with a ndarray of shape (43525,) representing the corresponding labels for the images in the training set. This array consists of a unique index per-class. And a mapping of the index to a human-readable class name can be found in all_classes.txt.
• test-images.npy - (10838 samples) A numpy file with an ndarray of shape (10779, 32, 32, 3) representing all the images in the test set.
• all_classes.txt - A file containing the mapping of the class index used in the train-labels.npy file, and the sample_submission.csv file to that of a human-readable class name. The file contains 38 lines, where the line index of the class-name in the file is the index of the class names in all the labels files.
• sample_submission.csv - A sample submission file to provide a reference about the expected file format for the submission system to work.

## 🚀 Submission

• Prepare a CSV containing header as class_index and the predicted class index [0-38) with the name as submission.csv.
• Sample submission format available as sample_submission.csv in the resources section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score and Accuracy Score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

## 📱 Contact

#### Notebooks

 0 [Getting Started Notebook] MINILEAVES Challange By gauransh_k About 1 year ago 0