classification

TensorFlow Classification API Modeled after TensorFlow Object Detection API

Adding your own model

1. add your new model file to extractors (see inception_v3 example)
2. have your model implement all the required methods and inherit FeatureExtractor
3. add your model to builders/feature_extractor_map.py (same as inception)

Usage

1. Create a config file, similar to the example pipeline_conifg.config
2. build protocol buffers with protoc classification/protos/*.proto --python_out=. from outside classification dir
3. cd in to classification directory and run export PYTHONPATH=`pwd`:$PYTHONPATH
4. from outside classification dir run python3 classification/trainer.py --pipeline_config {path_to_pipeline_config}

Data Format

This API utilizes TFRecord Format. There is a serializer in serializers that expects your data in the following format.

      inputs_dir/images:
                {image_name}.{image_file_extension}
      inputs_dir/labels/{image_name}.json:
              {
                  {annotation_1} : {category_name},
                  {annotation_2} : {category_name},
                  ...
              }
       inputs_dir/annotations.json (mapping between string label and number):
              {
                  {annotation_1} : {category_name: category_int64, ...},
                  {annotation_2} : {category_name: category_int64, ...},
                  ...
              }