Train custom object detectio model

AastaLLL · May 13, 2020, 7:35am

Hi,

1.
You can find the instruction to load custom model below:
Although this is for imagenet with C++, the procedure for detectnet-camera.py should be similar.

github.com

dusty-nv/jetson-inference/blob/master/docs/imagenet-custom.md#loading-custom-models-on-jetson

<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="imagenet-snapshot.md">Back</a> | <a href="detectnet-training.md">Next</a> | </sup><a href="../README.md#two-days-to-a-demo-digits"><sup>Contents</sup></a>
<br/>
<sup>Image Recognition</sup></p> 

# Loading Custom Models on Jetson

The [`imagenet-console`](../examples/imagenet-console/imagenet-console.cpp) and [`imagenet-camera`](../examples/imagenet-camera/imagenet-camera.cpp) programs that we used before also accept extended command line parameters for loading a custom model snapshot.  Set the `$NET` variable below to the path to your extracted snapshot:

``` bash
$ NET=networks/GoogleNet-ILSVRC12-subset

$ ./imagenet-console bird_0.jpg output_0.jpg \
--prototxt=$NET/deploy.prototxt \
--model=$NET/snapshot_iter_184080.caffemodel \
--labels=$NET/labels.txt \
--input_blob=data \
--output_blob=softmax
```

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2.
DIGITs cannot install on the environment without desktop GPU.
Since it need NCCL support, which requires a desktop GPU.

To install pyTorch on Jetson, please follow this topic:

To install Caffe on Jetson, please follow this tutorial:

Thanks.