🛠 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.
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
disease-class from low-resolution
Understand with code! Here is some code to get you started right away! 😄
The dataset consists of
43525 images of plant leaves at
32 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.
Following files are available in the
43466samples) A numpy file with an
(43525, 32, 32, 3)representing all the images in the training set.
43466samples) A numpy file with a
(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
10838samples) A numpy file with an
(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.npyfile, and the
sample_submission.csvfile 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.
- Prepare a CSV containing header as
class_indexand the predicted class index
[0-38)with the name as
- Sample submission format available as
sample_submission.csvin 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,
- 💪 Challenge Page: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/
- 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/discussion
- 🏆 Leaderboard: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/leaderboards
[Getting Started Notebook] MINILEAVES Challange
gauransh_kAbout 1 year ago