Ce projet consiste à développer une intelligence artificielle permettant de classifier des fruits grâce à une photo.
Lien vers le dataset: https://www.kaggle.com/sshikamaru/fruit-recognition
Total number of images: 22495.
Training set size: 16854 images (one fruit or vegetable per image).
Test set size: 5641 images (one fruit or vegetable per image).
Number of classes: 33 (fruits and vegetables).
Image size: 100x100 pixels.
Training data filename format: [fruit/vegetable name][id].jpg (e.g. Apple Braeburn100.jpg). Many images are also rotated, to help training.
Testing data filename format: [4 digit id].jpg (e.g. 0001.jpg)
Content train - the training folder that contains 33 subfolders in which training images for each fruit/vegetable are located. There is a total of 16854 images. test - the testing folder that contains 5641 testing images sampleSubmission.csv - a sample submission file in the correct format, with id number and string label
- numpy
- pandas
- matplotlib
- sklearn
- glob
- os
- datetime
- seaborn
- cv2 (opencv)