README.md

Evaluation of image classification model

Our evaluation service is a comprehensive tool that enables users to assess the accuracy of their TensorFlow Lite (.tflite) or Keras (.h5) image classification model. By uploading their model and a validation set, users can quickly and easily evaluate the performance of their model and generate various metrics, such as accuracy.

The evaluation service is designed to be fast, efficient, and accurate, making it an essential tool for anyone looking to evaluate the performance of their image classification model.

Table of contents

1. Configure the yaml file

To use this service and achieve your goals, you can use the user_config.yaml or directly update the evaluation_config.yaml file and use it. This file provides an example of how to configure the evaluation service to meet your specific needs.

Alternatively, you can follow the tutorial below, which shows how to evaluate your pre-trained image classification model using our evaluation service.

1.1 Set the model and the operation mode

As mentioned previously, all the sections of the YAML file must be set in accordance with this evaluation_config.yaml. In particular, operation_mode should be set to evaluation and the evaluation section should be filled as in the following example:

general:
   model_path: ../pretrained_models/mobilenetv2/ST_pretrainedmodel_public_dataset/flowers/mobilenet_v2_0.35_128_fft/mobilenet_v2_0.35_128_fft.h5

operation_mode: evaluation

In this example, the path to the MobileNet V2 model is provided in the model_path parameter.

1.2 Prepare the dataset

Information about the dataset you want use for evaluation is provided in the dataset section of the configuration file, as shown in the YAML code below.

dataset:
   dataset_name: flowers                                      # Dataset name. Optional, defaults to "<unnamed>".
   test_path: <test-set-root-directory>                       # Path to the root directory of the test set.
   check_image_files: False                                   # Enable/disable image file checking.

In this example, the path to the validation set is provided in the test_path parameter.

When working with a dataset for the first time, we suggest setting the check_image_files attribute to True. This will enable the system to load each image file and identify any corrupt, unsupported, or non-image files. The path to any problematic files will be reported.

In cases where there is no validation set path or test set provided to evaluate the model trained using the training service, the available data under the training_path directory is split into two to create a training set and a validation set. By default, 80% of the data is used for training and the remaining 20% is used for the validation set in the evaluation service.

If you want to use a different split ratio, you need to specify the percentage to be used for the validation set in the validation_split parameter (to ensure consistency in the training and evaluation process, you must specify the same validation_split parameter value in both the training and evaluation services), as shown in the YAML example below:

dataset:
   name: flowers
   class_names: [daisy, dandelion, roses, sunflowers, tulips]
   training_path: ../datasets/flower_photos
   validation_path:
   validation_split: 0.20
   test_path:

1.3 Apply preprocessing

The images from the dataset need to be preprocessed before they are presented to the network for evaluation. This includes rescaling and resizing. In particular, they need to be rescaled exactly as they were at training step. This is illustrated in the YAML code below:

preprocessing:
   rescaling: { scale: 1/127.5, offset: -1 }
   resizing: 
     aspect_ratio: "fit"
     interpolation: nearest
  color_mode: rgb

In this example, the pixels of the input images read in the dataset are in the interval [0, 255], that is UINT8. If you set scale to 1./255 and offset to 0, they will be rescaled to the interval [0.0, 1.0]. If you set scale to 1/127.5 and offset to -1, they will be rescaled to the interval [-1.0, 1.0].

The resizing attribute specifies the image resizing methods you want to use:

• The value of interpolation must be one of {"bilinear", "nearest", "bicubic", "area", "lanczos3", "lanczos5", "gaussian", "mitchellcubic"}.
• The value of aspect_ratio must be either "fit" or "crop". If you set it to "fit", the resized images will be distorted if their original aspect ratio is not the same as in the resizing size. If you set it to "crop", images will be cropped as necessary to preserve the aspect ratio.

The color_mode attribute must be one of "grayscale", "rgb" or "rgba".

1.4 Hydra and MLflow settings

The mlflow and hydra sections must always be present in the YAML configuration file. The hydra section can be used to specify the name of the directory where experiment directories are saved and/or the pattern used to name experiment directories. With the YAML code below, every time you run the Model Zoo, an experiment directory is created that contains all the directories and files created during the run. The names of experiment directories are all unique as they are based on the date and time of the run.

hydra:
   run:
      dir: ./experiments_outputs/${now:%Y_%m_%d_%H_%M_%S}

The mlflow section is used to specify the location and name of the directory where MLflow files are saved, as shown below:

mlflow:
   uri: ./experiments_outputs/mlruns

2. Evaluate your model

If you chose to modify the user_config.yaml you can evaluate the model by running the following command from the src/ folder:

python stm32ai_main.py 

If you chose to update the evaluation_config.yaml and use it then run the following command from the src/ folder:

python stm32ai_main.py --config-path ./config_file_examples/ --config-name evaluation_config.yaml

In case you want to evaluate the accuracy of the quantized model then benchmark it, you can either launch the evaluation operation mode followed by the benchmark service that describes in details how to proceed or you can use chained services like launching chain_eqeb example with command below:

python stm32ai_main.py --config-path ./config_file_examples/ --config-name chain_eqeb_config.yaml

3. Visualize the evaluation results

You can retrieve the confusion matrix generated after evaluating the float/quantized model on the test set by navigating to the appropriate directory within experiments_outputs/<date-and-time>.

You can also find the evaluation results saved in the log file stm32ai_main.log under experiments_outputs/<date-and-time>.