train_torchvision_mnasnet
About
Training process for MnasNet convolutional network.
Training process for MnasNet convolutional network.
🚀 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 workflowwf = Workflow()# Add dataset loaderdata_loader = wf.add_task(name="dataset_classification")data_loader.set_parameters({"dataset_folder": "path/to/dataset/folder"})# Add train algorithmtrain = wf.add_task(name="train_torchvision_mnasnet", auto_connect=True)# Launch your training on your datawf.run()
☀️ Use with Ikomia Studio
Ikomia Studio offers a friendly UI with the same features as the API.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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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 workflowwf = Workflow()# Add dataset loaderdata_loader = wf.add_task(name="dataset_classification")data_loader.set_parameters({"dataset_folder": "path/to/dataset/folder"})# Add train algorithmtrain = 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 datawf.run()
Developer
Ikomia
License
MIT License
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