Classifying New Images

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In Deep Learning Studio:

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Add new images to the dataset and refresh the results.

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Open the Inference tests tab to classify the new images and display the detailed results for these images.

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You can also export the results to CSV or export the heatmap from this tab.

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In the image viewer, you can enable or disable the heatmap visualization and configure how the heatmap is displayed (color map and transparency).

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Once the classifier is trained, call EClassifier::Classify to classify an Open eVision image.

This method returns a EClassificationResult object In a general content, the term object should be understood with the meaning of a class instance. In EasyObject, an object is a maximally-sized area of adjacent connected pixels belonging to the layer foreground.:

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EClassificationResult::GetBestLabel() returns the most probable label for the image.

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EClassificationResult::GetBestProbability() returns the probability associated with the most probable label.

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EClassificationResult::GetProbability(label) returns the probability associated with the given label.

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EClassificationResult::GetRanking(label) returns the ranking of the given label. The ranking goes from 1 (most probable) to EClassificationResult::GetNumLabels() (least probable).

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EClassificationResult::GetHeatmap and EClassificationResult::GetColorizedHeatmap return a heatmap highlighting the pixels that have contributed the most to get the most probable label.

- The heatmap is only available when the classifier property EClassifier::ComputeHeatmapWithResult is set to True.

- Use EClassificationResult::HasHeatmap to check if the result contains a heatmap.

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You can also do batch classification or directly classify a vector of Open eVision images:

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Images are processed together in groups determined by the batch size.

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On a GPU, it is usually much faster to classify a group of images than a single image.

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On a CPU, implement a multithread approach to accelerate the classification. In that case, each thread must have its own instance of EClassifier (see code snippets).

The batch classification has a tradeoff between the throughput (the number of images classified per second) and the latency (the time needed to obtain the result of an image): on a GPU, the higher the batch size, the higher the throughput and the latency. So, use batch classification to improve the classification speed at the cost of a longer time before obtaining the classification result of an image.

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Use EClassifier::GetHeatmap(img, label) to obtain a heatmap highlighting the pixels that contribute the most to a label.

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In some cases, this heatmap can provide a rough localization of the object corresponding to the label.

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The heatmap is colored, and the important parts are displayed in red.