train_readme.md

Use this blueprint to train a custom model on your own set of natural scene categories. The training and fine-tuning are performed on the pretrained VGG16-365 model (VGG16 model trained on the Places-365 dataset) with the data file the user uploads as a dataset. This blueprint also establishes an endpoint that can be used to classify scenes in images based on the newly trained model.

Users can use either the pretrained VGG16-365 model or a custom-trained model, the latter’s weights of which can be downloaded after the blueprint run. To train this model with your data, provide in the S3 Connector an img-dir dataset directory with multiple subdirectories containing the different classes of images, organized like the following:

• -class1 – first category of natural sceneries
• -class2 – second category of natural sceneries
• -class3 – third category of natural sceneries

Complete the following steps to train the scene-classifier model:

1. Click the Use Blueprint button. The cnvrg Blueprint Flow page displays.
2. In the flow, click the S3 Connector task to display its dialog.
   • Within the Parameters tab, provide the following Key-Value pair information:
     • Key: bucketname - Value: enter the data bucket name
     • Key: prefix - Value: provide the main path to the images folder
   • Click the Advanced tab to change resources to run the blueprint, as required.
3. Return to the flow and click the Train task to display its dialog.
   • Within the Parameters tab, provide the following Key-Value pair information:
     • Key: img_dir – Value: provide the path to the images folder including the S3 prefix
     • /input/s3_connector/<prefix>/scene_detection − ensure the path adheres this format NOTE: You can use prebuilt data examples paths already provided.
   • Click the Advanced tab to change resources to run the blueprint, as required.
4. Click the Run button. The cnvrg software launches the training blueprint as set of experiments, generating a trained scene-classifier model and deploying it as a new API endpoint.
5. Track the blueprint's real-time progress in its experiments page, which displays artifacts such as logs, metrics, hyperparameters, and algorithms.
6. Click the Serving tab in the project, locate your endpoint, and complete one or both of the following options:
   • Use the Try it Live section with any natural scene image to check the model.
   • Use the bottom integration panel to integrate your API with your code by copying in your code snippet.

A custom model and an API endpoint which can classify an image’s scenes have now been trained and deployed. To learn how this blueprint was created, click here.