YOLO V8 Classify Trained on ImageNet Competition Data

Identify the main object in an image

Training Set Information

ImageNet Large Scale Visual Recognition Challenge 2012 classification dataset, consisting of 1.2 million training images, with one thousand classes of objects.

Model Information

Examples

Download Example Notebook

Open in Wolfram Cloud

Resource retrieval

Get the pre-trained net:

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NetModel parameters

This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:

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Pick a non-default net by specifying the parameters:

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Pick a non-default uninitialized net:

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Basic usage

Classify an image:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/fb779ef2-8026-41a2-844d-f8b54ad9eedd"]

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The prediction is an Entity object, which can be queried:

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Get a list of available properties of the predicted Entity:

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Obtain the probabilities of the 10 most likely entities predicted by the net:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/d3f275ad-e60d-46e8-a15c-198390779830"]

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An object outside the list of the ImageNet classes will be misidentified:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/6c04f2a3-c69d-4bca-82f5-a2ad1063394f"]

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Obtain the list of names of all available classes:

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EntityValue[
NetExtract[
NetModel["YOLO V8 Classify Trained on ImageNet Competition Data"], "Output"][["Labels"]], "Name"]

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Feature extraction

Remove the last two layers of the trained net so that the net produces a vector representation of an image:

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Get a set of images:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/db2a9da7-ed6c-4683-9ddf-d085510abc30"]

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Visualize the features of a set of images:

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Transfer learning

Use the pre-trained model to build a classifier for telling apart images of bulbs and cameras. Create a test set and a training set:

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/a22e63e1-bd66-490c-9516-27f0fd2fddbf"]

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(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/19d1f9a6-a833-4daa-9740-c822cf5c5a14"]

Remove the last classification layers from the pre-trained net:

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Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:

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newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"bulb", "camera"}}]]

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Train on the dataset, freezing all the weights except for those in the "linearNew" layer (use TargetDevice -> "GPU" for training on a GPU):

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$trainedNet = NetTrain[newNet, trainSet, LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]$

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Perfect accuracy is obtained on the test set:

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Net information

Inspect the number of parameters of all arrays in the net:

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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Export to ONNX

Export the net to the ONNX format:

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Get the size of the ONNX file:

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The size is similar to the byte count of the resource object:

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Check some metadata of the ONNX model:

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Import the model back into Wolfram Language. However, the NetEncoder and NetDecoder will be absent because they are not supported by ONNX:

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Resource History

Date Created: 1 September 2023

Reference

G. Jocher, A. Chaurasia, J. Qiu, "YOLO by Ultralytics."
Available from: https://github.com/ultralytics/ultralytics
Rights: GNU General Public License