ResNet18 Image Classifier with Pre-Training in PyTorch

Image Classifier for the Image Classification problem category as per Ready Tensor specifications.

Project Description

Leveraging pre-trained models for image classification, this repository is an implementation for pretrained ResNet18.

The following are the requirements for using your data with this model:

• The training and testing data must consist of multiple directories. Each directory represents a class in the classification problem with the relevant images inside the directory. See /examples/mini_mnist.zip
• The train and test files across all different class directories must have unique names. The class directories must be named after the label of that class.
• No preprocessing is required for the images. Necessary preprocessing steps will be applied for the data to match the format expected by the pre-trained model.

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Project Structure

The following is the directory structure of the project:

• examples/: This directory contains example files for the mini-mnist dataset. Three files are included: mini_mnist.zip. You can extract the folders and place the training/testing folders in model_inputs_outputs/inputs.
• model_inputs_outputs/: This directory contains files that are either inputs to, or outputs from, the model. When running the model locally (i.e. without using docker), this directory is used for model inputs and outputs. This directory is further divided into:
  • /inputs/: This directory contains all the input files for this project, including the training and testing olders.
  • /model/artifacts/: This directory is used to store the model artifacts, such as trained models and their parameters.
  • /outputs/: The outputs directory contains sub-directories for error logs and prediction results.
• src/: This directory holds the source code for the project. It is further divided into various subdirectories:
  • paths.py/: This script includes paths to files used in the project.
  • train.ipynb: This notebook is used to train the model. It loads the data, preprocesses it, trains the model, and saves the artifacts in the path ./model_inputs_outputs/model/artifacts/.
  • predict.ipynb: This script is used to run batch predictions using the trained model. It loads the artifacts and creates and saves the predictions in a file called predictions.csv in the path ./model_inputs_outputs/outputs/predictions/.
• .gitignore: This file specifies the files and folders that should be ignored by Git.
• Dockerfile: This file is used to build the Docker image for the application.
• entry_point.sh: This file is used as the entry point for the Docker container. It is used to run the application. When the container is run using one of the commands train, predict, this script runs the corresponding script in the src folder to execute the task.
• LICENSE: This file contains the license for the project.
• requirements.txt for the main code in the src directory
• README.md: This file (this particular document) contains the documentation for the project, explaining how to set it up and use it.

Usage

In this section we cover the following:

• How to prepare your data for training
• How to run the model implementation locally (without Docker)
• How to run the model implementation with Docker

Preparing your data

• If you plan to run this model implementation on your own image classification dataset, you will need your training and testing data in a format similar to the one provided in /examples.

To run locally (without Docker)

• Create your virtual environment and install dependencies listed in requirements.txt which is inside the root directory.
• Move the two example folders (training and testing) in the examples directory into the ./model_inputs_outputs/inputs (or alternatively, place your custom dataset folders in the same location).
• Run the script src/train.ipynb to train the classifier model. This will save the model artifacts, in the path ./model_inputs_outputs/model/artifacts/.
• Run the script src/predict.ipynb to run batch predictions using the trained model. This script will load the artifacts and create and save the predictions in a file called predictions.csv in the path ./model_inputs_outputs/outputs/predictions/.

To run with Docker

1. Set up a bind mount on host machine: It needs to mirror the structure of the model_inputs_outputs directory. Place the data folders in the model_inputs_outputs/inputs directory.
2. Build the image. You can use the following command:
   docker build -t model_img .
   Here model_img is the name given to the container (you can choose any name).
3. Note the following before running the container for train, batch prediction:
   • The train, batch predictions tasks require a bind mount to be mounted to the path /opt/model_inputs_outputs/ inside the container. You can use the -v flag to specify the bind mount.
   • When you run the train or batch prediction tasks, the container will exit by itself after the task is complete.
   • When you run training task on the container, the container will save the trained model artifacts in the specified path in the bind mount. This persists the artifacts even after the container is stopped or killed.
   • When you run the batch prediction task, the container will load the trained model artifacts from the same location in the bind mount. If the artifacts are not present, the container will exit with an error.
   • Container runs as user 1000. Provide appropriate read-write permissions to user 1000 for the bind mount. Please follow the principle of least privilege when setting permissions. The following permissions are required:
     • Read access to the inputs directory in the bind mount. Write or execute access is not required.
     • Read-write access to the outputs directory and model directories. Execute access is not required.
4. Run training:
   • To run training, run the container with the following command container:
     docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs model_img train
     where model_img is the name of the container. This will train the model and save the artifacts in the model_inputs_outputs/model/artifacts directory in the bind mount.
5. To run batch predictions, place the prediction data folder in the model_inputs_outputs/inputs directory in the bind mount. Then issue the command:
   docker run -v <path_to_mount_on_host>/model_inputs_outputs:/opt/model_inputs_outputs model_img predict
   This will load the artifacts and create and save the predictions in a file called predictions.csv in the path model_inputs_outputs/outputs/predictions/ in the bind mount.

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Requirements

Dependencies for the main model implementation in src are listed in the file requirements.txt. You can install these packages by running the following command from the root of your project directory:

pip install -r requirements.txt

LICENSE

Please see the LICENSE file for more information.

Contact Information

Repository created by Ready Tensor, Inc. Visit https://www.readytensor.ai/