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# Transfer Learning with PyTorch
Transfer learning is a technique for re-training a DNN model on a new dataset, which takes less time than training a network from scratch. With transfer learning, the weights of a pre-trained model are fine-tuned to classify a customized dataset. In these examples, we'll be using the <a href="https://arxiv.org/abs/1512.03385">ResNet-18</a> and [SSD-Mobilenet](pytorch-ssd.md) networks, although you can experiment with other networks too.
<p align="center"><a href="https://arxiv.org/abs/1512.03385"><img src="https://github.com/dusty-nv/jetson-inference/raw/python/docs/images/pytorch-resnet-18.png" width="600"></a></p>
Although training is typically performed on a PC, server, or cloud instance with discrete GPU(s) due to the often large datasets used and the associated computational demands, by using transfer learning we're able to re-train various networks onboard Jetson to get started with training and deploying our own DNN models.
<a href=https://pytorch.org/>PyTorch</a> is the machine learning framework that we'll be using, and example datasets along with training scripts are provided to use below, in addition to a camera-based tool for collecting and labeling your own training datasets.
## Installing PyTorch
If you are [Running the Docker Container](aux-docker.md) or optionally chose to install PyTorch back when you [Built the Project](building-repo-2.md#installing-pytorch), it should already be installed on your Jetson to use. Otherwise, if you aren't using the container and want to proceed with transfer learning, you can install it now: