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Utility Scripts for TensorFlow Lite Object Detection

These scripts are used in the TFLite Training Colab to help with various steps of training a custom model. They can also be used as standalone tools.

Calculate model mAP - (calculate_map_cartucho.py)

Calculate your TFLite detection model's mAP score! I'll share instructions on how to use this outside the Colab notebook later.

This tool uses the main.py script from Cartucho's excellent repository, which takes in ground truth data and detection results to calculate average precision at a certain IoU threshold. The calculate_map_cartucho.py script performs the mAP calculation at multiple IoU thresholds to determine the COCO metric for average mAP @ 0.5:0.95.

Split images into train, test, and validation sets - (train_val_test.py)

This script takes a folder full of images and randomly splits them between train, test, and validation folders. It does an 80%/10%/10% split by default, but this can be modified by changing the train_percent, test_percent, and val_percent variables in the code.

Create CSV annotation file - (create_csv.py)

This script creates a single CSV data file from a set of Pascal VOC annotation files.

Original credit for the script goes to datitran.

Create TFRecord file - (create_tfrecord.py)

This script creates TFRecord files from a CSV annotation data file and a folder of images. TFRecords are the data format required by the TensorFlow Object Detection API for training.

Original credit for the script goes to datitran.