All the metrics used are available in the sklearn package, see the documentation at https://scikit-learn.org/stable/api/sklearn.metrics.html.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6372737%2F2d0c8c299f63bb8a5823683346ba1ba8%2FImage2.jpg?generation=1659570752665846&alt=media">

The dataset contains 2 folders: one with the test data and the other one with train data. The test-train-split ratio is 0.14, with the test dataset containing 114 images and the train dataset containing 711. The images have a resolution of 240x240 pixels in RGB color model. Both the folders contain 3 classes:

• Adidas
• Converse
• Nike ** ** ### Inspiration

This dataset is ideal for performing multiclass classification with deep neural networks like CNNs or simpler machine learning classification models. You can use Tensorflow, his high-level API keras, Sklearn, PyTorch or other deep/machine learning libraries to building the model from scratch or, as an alternative, fetching pretrained models as well as fine-tuning them. It is also possible to modify the size of the images or preprocessing them using OpenCV , and check if the accuracy of the model improves.
Remember to upvote if you found the dataset useful :).

Collection methodology

The dataset was obtained downloading images from Google images.

The images with a .webp format were transformed into .jpg images. The obtained images were randomly shuffled and resized so that all the images had a resolution of 240x240 pixels. Then, they were split into train and test datasets and saved.