• Make sure you have the prerequisites for the Object Detection API installed. The directions for installations can be found here
• Download the training and evaluation images in this directory

Run the following commands

 # Unzip the contents
 unzip characters.zip
 unzip eval_images.zip
 
 # Change the filneame attribute in the train and eval csvs to point to the correct location of the images
 python change_csv.py

Make sure the images are inside the CharacterFinder/ directory

The following instuctions only creates training and evaluation records for one character - Santa. If you want to train on more characters, follow these instructions first

• After the csv points to the correct location, we can generate the record files

# Generate training record
python --csv_input train.csv --output_path train.record --label_map_path characters_label_map.pbtext 

# Generate evaluation record
python --csv_input eval.csv --output_path eval.record --label_map_path characters_label_map.pbtext 

For training you need to construct an object-detection training pipeline.

• You can use any of the config files present in object_detection/samples/configs/ as basis
• Adjust the number of classes depending on the number of character you are training on
• It is recommended to train your model from a pre-trained checkpoint. Tensorflow provides several pre-trained checkpoints which can be found here
• Changes in the config file:
  • Change the fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt" to point to the checkpoint you want to use
  • In

   train_input_reader: {
    tf_record_input_reader {
      input_path: "PATH_TO_BE_CONFIGURED/train.record"
    }
    label_map_path: "PATH_TO_BE_CONFIGURED/characters_label_map.pbtxt"
  }
  

  change the input_path to point to the train.record file generated in the previous step and change the label_map_path to point to the appropriate label map