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README_CLASSIFIER.md

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Image Classification

Current repository includes training and evaluation tools for the image classification task. Use the following prepared configuration files to train the model on:

  • ImageNet dataset (configs/classification/imagenet_rmnet.yml)

Data Preparation

Assume the following structure of data:

    |-- data_dir
         |-- images
            image_000000.png
            image_000001.png
         train_data.txt
         test_data.txt

Both data files, train_data.txt and test_data.txt, describe data to train and evaluate a model. Each row in a data file represents a single source in the following format: path_to_image image_label.

Train a Model

To train an image-classification model from scratch, run the command:

python2 tools/models/train.py -c configs/classification/imagenet_rmnet.yml \ # path to config file
                              -t <PATH_TO_DATA_FILE> \                       # file with train data paths
                              -l <PATH_TO_LOG_DIR> \                         # directory for logging
                              -b 4 \                                         # batch size
                              -n 1 \                                         # number of target GPU devices

NOTE: To initialize the model from the pretrained model weights, specify the -i key as a path to init weights and set the valid --src_scope key value: to initialize the model after pretraining on any other classification dataset with the DATASET_NAME name, set --src_scope "DATASET_NAME/rmnet".

The command to run the training procedure from the pretrained model:

python2 tools/models/train.py -c configs/classification/imagenet_rmnet.yml \ # path to config file
                              -t <PATH_TO_DATA_FILE> \                       # file with train data paths
                              -l <PATH_TO_LOG_DIR> \                         # directory for logging
                              -b 4 \                                         # batch size
                              -n 1 \                                         # number of target GPU devices
                              -i <PATH_TO_INIT_WEIGHTS> \                    # initialize model weights
                              --src_scope "DATASET_NAME/rmnet"               # name of scope to load weights from

Model Evaluation

To evaluate the quality of the trained image-classification model, prepare the test data according to the instruction.

python2 tools/models/eval.py -c configs/classification/imagenet_rmnet.yml \ # path to config file
                             -v <PATH_TO_DATA_FILE> \                       # file with test data paths
                             -b 4 \                                         # batch size
                             -s <PATH_TO_SNAPSHOT> \                        # snapshot model weights