cocorobo-ai-tool 0.1.3

CocoRobo AI Training Tools

CocoRobo AI Training Tool

Requirements

Pacakages:

• scikit-image==0.16.2
• matplotlib>=3.3.0
• numpy>=1.19.5

System Requirements:

• Nvidia CUDA & cuDNN installed
• Ubuntu 16+

Usage

After this tool is installed, follow the instruction below to create your own model:

from cocoroboai import init, tool, train
import os, time

configuration = {
    "RootPath": os.getcwd(),
    "ConfigurationPath": os.getcwd() + "/config",
    "DatasetPath": os.getcwd() + "/dataset",
    "DarknetPath": os.getcwd() + "/darknet-linux"
}

init = init(configuration)
tool, train = tool(), train()

# Resize a raw dataset to 448 * 448 px dimension
log = tool.resize_dataset(
    path = dataset_path
)
print(log)

# Process labeled dataset
project_name = project_name
labeled_dataset_path = labeled_dataset_path

log = tool.get_labeled_dataset_from_local(
    name = project_name, 
    path = labeled_dataset_path
)
print(log)

processed_dataset_name = log["Response"]["DatasetName"]
print(processed_dataset_name)

# Get dataset ready for training
log = train.prepare(
    name = processed_dataset_name
)
print(log)

# Start training
log = train.start(
    name = processed_dataset_name
)
print(log)

# Checkout training status
status = train.status(
    name = processed_dataset_name
)
print(status)

# Constantly checking training status until the avg loss is lower than 0.06
while True:
    status = train.status(
        name = processed_dataset_name
    )
    time.sleep(1)

    if len(status["Response"]["LatestEpochStatus"]) > 0:
        latest_avg_loss = status["Response"]["LatestEpochStatus"]
        print("Latest epoch info:\t", latest_avg_loss)
        print("Latest weights info:\t", status["Response"]["WeightsInfo"])

        if float(latest_avg_loss["AverageLoss"]) <= 0.06:
            log = train.stop(
                name = processed_dataset_name
            )
            print(log)
            break

# Test trained model with default model and images
log = train.test(
    name = processed_dataset_name
)
print(log)

# Get training statistics
log = train.get_statistics(
    name = processed_dataset_name
)
for weight in log["Response"]["ModelInfo"]["Weights"]["WeightsList"]:
    print(weight["Iteration"])
    if weight["Iteration"] == "last":
        model_name = weight["ModelName"]
print(model_name)

# Test trained model with specified model and images
log = train.test(
    name = processed_dataset_name,
    model_name = model_name,
    image_path = image_path
)
print(log)

# Export to kmodel file
log = train.export_kmodel(
    name = name,
    model_name = model_name
)
print(log)