Re-training inception v4

EpicMario71 · November 3, 2021, 7:10pm

is it possible to retrain the inception v4 and convert it to onnx through the tools inside jetson-interface?

AastaLLL · November 4, 2021, 3:05am

Hi,

Below is a tutorial to retrain and convert a ResNet model.
Please update the pre-train model directly for your use case.

github.com

dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md

<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="pytorch-transfer-learning.md">Back</a> | <a href="pytorch-plants.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>Transfer Learning - Classification</sup></s></p>

# Re-training on the Cat/Dog Dataset

The first model that we'll be re-training is a simple model that recognizes two classes:  cat or dog.

<img src="https://github.com/dusty-nv/jetson-inference/raw/python/docs/images/pytorch-cat-dog.jpg" width="700">

Provided below is an 800MB dataset that includes 5000 training images, 1000 validation images, and 200 test images, each evenly split between the cat and dog classes.  The set of training images is used for transfer learning, while the validation set is used to evaluate classification accuracy during training, and the test images are to be used by us after training completes.  The network is never directly trained on the validation and test sets, only the training set.

The images from the dataset are made up of many different breeds of dogs and cats, including large felines like tigers and mountain lions since the amount of cat images available was a bit lower than dogs.  Some of the images also picture humans, which the detector is essentially trained to ignore as background and focus on the cat vs. dog content.

To get started, first make sure that you have [PyTorch installed](pytorch-transfer-learning.md#installing-pytorch) on your Jetson, then download the dataset below and kick off the training script.  After that, we'll test the re-trained model in TensorRT on some static images and a live camera feed. 

## Downloading the Data

During this tutorial, we'll store the datasets on the host device under `jetson-inference/python/training/classification/data`, which is one of the directories that is automatically [mounted into the container](aux-docker.md#mounted-data-volumes).  This way the dataset won't be lost when you shutdown the container.

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Thanks.

EpicMario71 · November 4, 2021, 6:08pm

I tried to import the inception-v2.onnx model into my object detection program but it gives me this error: net = jetson.inference.detectNet(argv=[‘–model=model/inception-v2-3.onnx’, ‘–labels=model/labels.txt’, ‘–input-blob=input_0’, ‘–output-cvg=scores’, ‘–output-bbox=boxes’])
Exception: jetson.inference – detectNet failed to load network

AastaLLL · November 8, 2021, 2:43am

Hi,

Could you share the complete error log with us?
In general, you should update the model information like below:

github.com

dusty-nv/jetson-inference/blob/master/c/detectNet.cpp#L249


      
          	else if( networkType == COCO_DOG )

          		return Create("networks/DetectNet-COCO-Dog/deploy.prototxt", "networks/DetectNet-COCO-Dog/snapshot_iter_38600.caffemodel", 0.0f, "networks/DetectNet-COCO-Dog/class_labels.txt", threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device, allowGPUFallback );

          #if NV_TENSORRT_MAJOR > 4

          	else if( networkType == SSD_INCEPTION_V2 )

          		return Create("networks/SSD-Inception-v2/ssd_inception_v2_coco.uff", "networks/SSD-Inception-v2/ssd_coco_labels.txt", threshold, "Input", Dims3(3,300,300), "NMS", "NMS_1", maxBatchSize, precision, device, allowGPUFallback);

          	else if( networkType == SSD_MOBILENET_V1 )

          		return Create("networks/SSD-Mobilenet-v1/ssd_mobilenet_v1_coco.uff", "networks/SSD-Mobilenet-v1/ssd_coco_labels.txt", threshold, "Input", Dims3(3,300,300), "Postprocessor", "Postprocessor_1", maxBatchSize, precision, device, allowGPUFallback);

          	else if( networkType == SSD_MOBILENET_V2 )

          		return Create("networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff", "networks/SSD-Mobilenet-v2/ssd_coco_labels.txt", threshold, "Input", Dims3(3,300,300), "NMS", "NMS_1", maxBatchSize, precision, device, allowGPUFallback);

          #endif

          	else

          		return NULL;

          #else

          	if( networkType == PEDNET_MULTI )

          		return Create("networks/multiped-500/deploy.prototxt", "networks/multiped-500/snapshot_iter_178000.caffemodel", "networks/multiped-500/mean.binaryproto", threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device, allowGPUFallback );

          	else if( networkType == FACENET )

          		return Create("networks/facenet-120/deploy.prototxt", "networks/facenet-120/snapshot_iter_24000.caffemodel", NULL, threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device, allowGPUFallback );

          	else if( networkType == PEDNET )

          		return Create("networks/ped-100/deploy.prototxt", "networks/ped-100/snapshot_iter_70800.caffemodel", "networks/ped-100/mean.binaryproto", threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device, allowGPUFallback );

          	else if( networkType == COCO_AIRPLANE )

          		return Create("networks/DetectNet-COCO-Airplane/deploy.prototxt", "networks/DetectNet-COCO-Airplane/snapshot_iter_22500.caffemodel", "networks/DetectNet-COCO-Airplane/mean.binaryproto", threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device, allowGPUFallback );

Thanks.

system · December 1, 2021, 3:21am

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Re-training inception v4

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