train_torchvision_mnasnet

train_torchvision_mnasnet

About

1.3.2
MIT

Training process for MnasNet convolutional network.

Task: Classification
MnasNet
classification
train
mobile
edge

Training process for MnasNet convolutional network.

Rock paper scissors

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add dataset loader
data_loader = wf.add_task(name="dataset_classification")

data_loader.set_parameters({"dataset_folder": "path/to/dataset/folder"})

# Add train algorithm
train = wf.add_task(name="train_torchvision_mnasnet", auto_connect=True)

# Launch your training on your data
wf.run()

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

  • If you haven't started using Ikomia Studio yet, download and install it from this page.

  • For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

  • model_name (str) - default 'resnet18': Name of the pre-trained model.
    • There are over 700 timm models. You can list them using: timm.list_models()
  • input_size (int) - default '224': Size of the input image.
  • epochs (int) - default '15': Number of complete passes through the training dataset.
  • batch_size (int) - default '8': Number of samples processed before the model is updated.
  • learning_rate (float) - default '0.001': Step size at which the model's parameters are updated during training.
  • output_folder (str, optional): path to where the model will be saved.
  • num_workers (int) - default '0': How many parallel subprocesses you want to activate when you are loading all your data during your training or validation.
  • weight_decay (float) - default '1e-4': Amount of weight decay, regularization method.
  • export_pth (bool) - default 'True'
  • export_onnx (bool) - default 'False'

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add dataset loader
data_loader = wf.add_task(name="dataset_classification")

data_loader.set_parameters({"dataset_folder": "path/to/dataset/folder"})

# Add train algorithm
train = wf.add_task(name="train_torchvision_mnasnet", auto_connect=True)
train.set_parameters({
"batch_size": "8",
"epochs": "5",
"input_size": "240",
"momentum": "0.9",
"learning_rate": "0.001",
"weight_decay": "1e-4",
"export_pth": "True",
"export_onnx": "False",
})

# Launch your training on your data
wf.run()

Developer

  • Ikomia
    Ikomia

License

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