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ImageAI : Custom Prediction Model Training

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Introducing Jarvis and TheiaEngine.

We the creators of ImageAI are glad to announce 2 new AI projects to provide state-of-the-art Generative AI, LLM and Image Understanding on your personal computer and servers.

Install Jarvis on PC/Mac to setup limitless access to LLM powered AI Chats for your every day work, research and generative AI needs with 100% privacy and full offline capability.

Visit https://jarvis.genxr.co to get started.

TheiaEngine, the next-generation computer Vision AI API capable of all Generative and Understanding computer vision tasks in a single API call and available via REST API to all programming languages. Features include

  • Detect 300+ objects ( 220 more objects than ImageAI)
  • Provide answers to any content or context questions asked on an image
    • very useful to get information on any object, action or information without needing to train a new custom model for every tasks
  • Generate scene description and summary
  • Convert 2D image to 3D pointcloud and triangular mesh
  • Semantic Scene mapping of objects, walls, floors, etc
  • Stateless Face recognition and emotion detection
  • Image generation and augmentation from prompt
  • etc.

Visit https://www.genxr.co/theia-engine to try the demo and join in the beta testing today.

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ImageAI provides the most simple and powerful approach to training custom image prediction models using state-of-the-art SqueezeNet, ResNet50, InceptionV3 and DenseNet which you can load into the imageai.Classification.Custom.CustomImageClassification class. This allows you to train your own model on any set of images that corresponds to any type of objects/persons. The training process generates a JSON file that maps the objects types in your image dataset and creates lots of models. You will then pick the model with the highest accuracy and perform custom image prediction using the model and the JSON file generated.

TABLE OF CONTENTS

Custom Model Training

Because model training is a compute intensive tasks, we strongly advise you perform this experiment using a computer with a NVIDIA GPU and the GPU version of Tensorflow installed. Performing model training on CPU will my take hours or days. With NVIDIA GPU powered computer system, this will take a few hours. You can use Google Colab for this experiment as it has an NVIDIA K80 GPU available.

To train a custom prediction model, you need to prepare the images you want to use to train the model. You will prepare the images as follows:

  1. Create a dataset folder with the name you will like your dataset to be called (e.g pets)
  2. In the dataset folder, create a folder by the name train
  3. In the dataset folder, create a folder by the name test
  4. In the train folder, create a folder for each object you want to the model to predict and give the folder a name that corresponds to the respective object name (e.g dog, cat, squirrel, snake)
  5. In the test folder, create a folder for each object you want to the model to predict and give the folder a name that corresponds to the respective object name (e.g dog, cat, squirrel, snake)
  6. In each folder present in the train folder, put the images of each object in its respective folder. This images are the ones to be used to train the model To produce a model that can perform well in practical applications, I recommend you about 500 or more images per object. 1000 images per object is just great
  7. In each folder present in the test folder, put about 100 to 200 images of each object in its respective folder. These images are the ones to be used to test the model as it trains
  8. Once you have done this, the structure of your image dataset folder should look like below: 
    pets//train//dog//dog-train-images
pets//train//cat//cat-train-images
pets//train//squirrel//squirrel-train-images
pets//train//snake//snake-train-images 
pets//test//dog//dog-test-images
pets//test//cat//cat-test-images
pets//test//squirrel//squirrel-test-images
pets//test//snake//snake-test-images
  9. Then your training code goes as follows: 
    from imageai.Classification.Custom import ClassificationModelTrainer
model_trainer = ClassificationModelTrainer()
model_trainer.setModelTypeAsResNet50()
model_trainer.setDataDirectory("pets")
model_trainer.trainModel(num_objects=4, num_experiments=100, enhance_data=True, batch_size=32, show_network_summary=True)

Yes! Just 5 lines of code and you can train any of the available 4 state-of-the-art Deep Learning algorithms on your custom dataset. Now lets take a look at how the code above works.

from imageai.Classification.Custom import ClassificationModelTrainer
model_trainer = ClassificationModelTrainer()
model_trainer.setModelTypeAsResNet50()
model_trainer.setDataDirectory("pets")

In the first line, we import the ImageAI model training class, then we define the model trainer in the second line, we set the network type in the third line and set the path to the image dataset we want to train the network on.

model_trainer.trainModel(num_experiments=100, batch_size=32)

In the code above, we start the training process. The parameters stated in the function are as below:

  • num_experiments : this is to state the number of times the network will train over all the training images, which is also called epochs
  • batch_size : This is to state the number of images the network will process at ones. The images are processed in batches until they are exhausted per each experiment performed.

When you start the training, you should see something like this in the console:

==================================================
Training with GPU
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Epoch 1/100
----------
100%|█████████████████████████████████████████████████████████████████████████████████| 282/282 [02:15<00:00,  2.08it/s]
train Loss: 3.8062 Accuracy: 0.1178
100%|███████████████████████████████████████████████████████████████████████████████████| 63/63 [00:26<00:00,  2.36it/s]
test Loss: 2.2829 Accuracy: 0.1215
Epoch 2/100
----------
100%|█████████████████████████████████████████████████████████████████████████████████| 282/282 [01:57<00:00,  2.40it/s]
train Loss: 2.2682 Accuracy: 0.1303
100%|███████████████████████████████████████████████████████████████████████████████████| 63/63 [00:20<00:00,  3.07it/s]
test Loss: 2.2388 Accuracy: 0.1470

Let us explain the details shown above:

  1. The line Epoch 1/100 means the network is training the first experiment of the targeted 100
  2. The line 1/25 [>.............................] - ETA: 52s - loss: 2.3026 - acc: 0.2500 represents the number of batches that has been trained in the present experiment
  3. The best model is automatically saved to <dataset-directory>/models>

Once you are done training your custom model, you can use the "CustomImageClassification" class to perform image prediction with your model. Simply follow the link below. imageai/Classification/CUSTOMCLASSIFICATION.md

Documentation

We have provided full documentation for all ImageAI classes and functions. Find links below: