batch_readme.md

Use this blueprint to run in batch mode a pretrained tailored model that detects human body poses in images using your custom data. The model can be trained using this counterpart’s training blueprint, after which the trained model can be uploaded to the S3 Connector. To train this model with your data, create a folder also located in the S3 Connector containing the images (with humans in various poses) on which to train the model.

Complete the following steps to run the pose-detector blueprint in batch mode:

1. Click Use Blueprint button. The cnvrg Blueprint Flow page displays.
2. Click the S3 Connector task to display its dialog.
   • Within the Parameters tab, provide the following Key-Value pair information:
     • Key: bucketname − Value: provide 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. Click the Batch-Predict task to display its dialog.
   • Within the Parameters tab, provide the following Key-Value pair information:
     • Key: test_dir_img − Value: provide the S3 location containing all the test images in the following format: /input/s3_connector/model_files/pose_detection/test_images/
     • Key: model_weights − Value: provide the S3 location containing the model weights in the following format: /input/s3_connector/model_files/pose_detection/generic/weights.best.hdf5
     • Key: class_names − Value: provide the S3 location containing the class names in the following format: /input/s3_connector/model_files/pose_detection/generic/class_names.csv NOTE: You can use the prebuilt example data paths provided.
   • Click the Advanced tab to change resources to run the blueprint, as required.
4. Click the Run button. The cnvrg software deploys a pose-detector model that detects human poses, their classifications, and their locations in images.
5. Track the blueprint’s real-time progress in its Experiments page, which displays artifacts such as logs, metrics, hyperparameters, and algorithms.
6. Select Batch Inference > Experiments > Artifacts and locate the bounding box images and output CSV file.
7. Select the final_output.csv File Name, click the right Menu icon, and click Open File to view the output CSV file.

A custom model that can detect an image’s human body poses has now been deployed in batch mode. To learn how this blueprint was created, click here.