Package: intel-transfer-learning-tool
Note: You may find it easier to read about Intel Transfer Learning tool, follow the Get
Started guide, and browse the API material from our published documentation sitehttps://intelai.github.io/transfer-learning.Intel® Transfer Learning ToolTransfer learning workflows use the knowledge learned by a pre-trained model on
a large dataset to improve the performance of a related problem with a smaller
dataset.What is Intel® Transfer Learning ToolIntel® Transfer Learning Tool makes it easier and faster for you to
create transfer learning workflows across a variety of AI use cases. Its
open-source Python* library leverages public pretrained model hubs,
Intel-optimized deep learning frameworks, and your custom dataset to efficiently
generate new models optimized for Intel hardware.This project documentation provides information, resource links, and instructions for the Intel
Transfer Learning Tool as well as Jupyter* notebooks and examples that
demonstrate its usage.Features:Supports PyTorch* and TensorFlow*Select from over100 computer vision and natural language processing modelsfrom
Torchvision, PyTorch Hub, TensorFlow Hub, Keras, and Hugging FaceUse cases include:Image ClassificationText ClassificationText GenerationVision Anomaly DetectionUse your own custom dataset or get started quickly with built-in datasetsAutomatically create a trainable classification layer customized for your datasetPre-process your dataset using scaling, cropping, batching, and splittingUse APIs for prediction, evaluation, and benchmarkingExport your model for deployment or resume training from checkpointsIntel Optimizations:Boost performance with Intel® Optimization for TensorFlow and Intel® Extension for PyTorchQuantize to INT8 to reduce model size and speed up inference using Intel® Neural CompressorOptimize model for FP32 inference using Intel Neural CompressorReduce training time with auto-mixed precision for select hardware platformsFurther reduce training time with multinode trainingHow the Intel Transfer Learning Tool WorksThe Intel Transfer Learning Tool lets you train AI models with TensorFlow or
PyTorch using either no-code command line interface (CLI) commands at a bash
prompt, or low-code application programming interface (API) calls from a Python
script.Use your own dataset or select an existing image or text dataset listed in thepublic datasetsdocumentation. Construct your own CLI or API commands for training, evaluation,
and optimization using the TensorFlow or PyTorch framework, and finally export
your saved model optimized for inference on Intel CPUs.An overview of the Intel Transfer Learning Tool flow is shown in this
figure:Intel Transfer Learning Tool FlowGet StartedCheck out theGet Started Guidewhich will walk you through the
steps to check system requirements, install, and then run the tool with a couple of
examples showing no-code CLI and low-code API approaches. After that, you can check out
these additional CLI and APIExamples.As described in theGet Started Guide, once you have a Python
environment set up, you do a basic install of the Intel Transfer Learning
Tool. Here are some examples of commands you will find in theGet Started Guide:pip install intel-transfer-learning-toolThen you can use the Transfer Learning Tool CLI interface (tlt) to train a
TensorFlow image classification model (resnet_v1_50), download and use an
existing built-in dataset (tf_flowers), and save the trained model to/tmp/outputusing this one command:tlt train --framework tensorflow --model-name resnet_v1_50 --dataset-name tf_flowers \
   --output-dir /tmp/output --dataset-dir /tmp/dataUsetlt --helpto see the list of CLI commands.  More detailed help for each
command can be found using, for example,tlt train --help.Note on Evaluation and BiasIntel Transfer Learning Tool provides standard evaluation metrics such as accuracy and loss for validation/test/train sets. While important, it's essential to acknowledge that these metrics may not explicitly capture biases. Users should be cautious and consider potential biases by analyzing disparities in the data and model prediction. Techniques such as confusion matrices, PR curves, ROC curves, local attribution-based andgradCAMexplanations, can all be good indicators for bias. Clear documentation of model behavior and performance is also crucial for iterative bias mitigation.Intel® Explainable AI Toolsprovides components that demonstrate the aformentioned techniques withExplainer, a simple API providing post-hoc model distillation and visualization methods, as well as TheModel Card Generatorwhich provides an interactive HTML report that containing these workflows and demonstrations of model behavior.SupportThe Intel Transfer Learning Tool team tracks bugs and enhancement requests usingGitHub issues. Before submitting a
suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.SeeLegal Informationfor Disclaimers, Trademark, and Licensing information.