The dataset contains images of more than 130 fruits & vegetables.

From the above link you will be able to download a zip file named ‘fruits_data.zip’. After you extract this zip file, you will get four files:

train - contains all the images that are to be used for training your model.
Training_set.csv - this csv file has contains the labels for the training images
test - contains 9000+ images. For these images you are required to make predictions
Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you submit are with their image’s filename in the same order as given in this file.
sample_submission.csv- this is the sample submission file for the data sprint

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below:

label

Banana Lady Finger

Orange

Quince

Cocos

Tomato 1

Apple Golden 1

Kiwi

Cactus fruit

.

.

.

Etc.

Note that the header name should be ‘label’ else it will throw an evaluation error.

Steps to follow to submit your notebook:

Go to the Notebook section on this challenge page
Click on the 'Upload Notebook' button on top left corner as shown in the image below:
Make sure to make the visibility of your notebook as 'public' while uploading the notebook.
Provide Notebook Title, Summary, add some tags, go to next, chose a file and click on Upload.

Fruit recognition from images using deep learning by Horea Mureșan and Mihai Oltean

Data Sprint #41: Fruits Recognition