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With Xailient Console, you can can either use the pre-loaded datasets, upload your own dataset, or combine different datasets inorder to train your AI.

Pre-loaded Dataset

From the navigation menu on the left, under Datasets click Pre-Loaded.

Here, you will see a list of all the public datasets that we have pre-loaded and made available to you.

Upload your own Dataset

  1. To prepare your images for uploading to the Console, place all the images into a folder. All images must be either in .jpg or .png format.

  2. Now compress the folder into a .zip or .gz file.

  3. Prepare labels for your dataset in CSV format. Please ensure your CSV has only these SIX columns. Spelling matters and it's case sensitive. The order doesn't matter.

    E.g. ['image_name', 'class', 'xmin', 'ymin', 'xmax', 'ymax']

    Here is what the CSV file should look like:

  4. Go to the Console. From the navigation menu on the left, under Dataset click Add.

  5. Enter the name of the dataset, select the zip file yor prepared eariler and click UPLOAD.

  6. When the upload begins, do not close the browser to ensure that the dataset is uploaded successfully.

  7. Now upload your label file you prepared earlier.

  8. When the lable file upload is completed, you will then get redirected to MANAGE DATASETS page where your dataset is currently getting sanity checked before it becomes available. This can take a few minutes. Refresh the page if it takes longer than 20 minutes!

    If the sanity check passes without errors, it will get automatically listed to the above list and will now be ready to train on.

    If the sanity check fails then the ERROR DETAILS could explain the reason. For example, here we have uploaded a CSV labels file where not a single image in the .zip file has a corresponding annotation. So make sure at least 1 row in the CSV file corresponds to an image filename in the .zip file. You can correct these errors and re-upload the CSV by clicking UPDATE LABEL.

    Sometimes the sanity check will pass with partial success because we detected that some images don't contain labels and vice versa, which is acceptable since this means the rest have corresponding labels. In this case you acknowledge the error and can manually approve it by clicking the APPROVE button.

    This approved dataset will then get listed above and is now ready to train on.