My nano crashes and reboots by itself while installing Pytorch.
(I have tried more than 5 times. The result is always same.
Nano crashed and rebooted without any trace. Thus, I don’t have any trace to show you except what I did:
$ cd jetson-inference/build
From the page,
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="depthnet.md">Back</a> | <a href="pytorch-cat-dog.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Transfer Learning</sup></s></p>
# 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:
``` bash
show original
Yes, I read one discussion, and using an argument for the installation script. (Although the person’s issue was not caching, but not being able to install. Anyway, I have tried, but it does not make any difference.)
I have also tried
Same result did I got!
I am at the verge of quitting. Please help me.
Thanks!
I was able to make it work.
Flash SD card for Nano
Initial Ubuntu setup
Just do Quick Reference:in Building the Project from Source pagejetson-inference/building-repo-2.md at master · dusty-nv/jetson-inference · GitHub GitHub - dusty-nv/jetson-inference: Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
Then move on to the next page (Classifying Images with ImageNet)
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="building-repo-2.md">Back</a> | <a href="imagenet-example-python-2.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Image Recognition</sup></p>
# Classifying Images with ImageNet
There are multiple types of deep learning networks available, including recognition, detection/localization, and semantic segmentation. The first deep learning capability we're highlighting in this tutorial is **image recognition**, using classifcation networks that have been trained on large datasets to identify scenes and objects.
<img src="https://github.com/dusty-nv/jetson-inference/raw/pytorch/docs/images/imagenet.jpg" width="1000">
The [`imageNet`](../c/imageNet.h) object accepts an input image and outputs the probability for each class. Having been trained on the ImageNet ILSVRC dataset of **[1000 objects](../data/networks/ilsvrc12_synset_words.txt)**, the GoogleNet and ResNet-18 models were automatically downloaded during the build step. See [below](#downloading-other-classification-models) for other classification models that can be downloaded and used as well.
As an example of using the [`imageNet`](../c/imageNet.h) class, we provide sample programs for C++ and Python:
- [`imagenet.cpp`](../examples/imagenet/imagenet.cpp) (C++)
- [`imagenet.py`](../python/examples/imagenet.py) (Python)
These samples are able to classify images, videos, and camera feeds. For more info about the various types of input/output streams supported, see the [Camera Streaming and Multimedia](aux-streaming.md) page.
show original
Just follow the examples or just move to the Transfer Learning with PyTorch page.
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
<p align="right"><sup><a href="depthnet.md">Back</a> | <a href="pytorch-cat-dog.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Transfer Learning</sup></s></p>
# 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:
``` bash
show original
I have tried twice. It is repeatable. Thus, I am sharing this.
Thanks!