CODEWITHSUNDEEP
ibm-cloudibm-watsonvisual-recognition
Ravi
Ravi

Reputation: 145

similarity search API of Watson Visual Recognition related to bulk volume of images

We have a customer requirement to search similar images in a collection using Watson Visual Recognition. The documentation mentions that each collection can contain 1 million images. Thus, I have the following questions:

a) What is the maximum size of the image?

b) Each image upload takes up to 1 second and the standard plan has a limit of 25000 images per day. So, can only 25k images added to the collection/day?

c) The customer has about 2 million images. How can we upload the images faster?

d) Is there a separate plan available for bulk volumes?

Upvotes: 0

Views: 262

Answers (2)

Matt Hill
Matt Hill

Reputation: 1106

Ravi, I see you posted your question on developerWorks too - please see my answer here: https://developer.ibm.com/answers/questions/379227/similarity-search-api-of-watson-visual-recognition/

Upvotes: 0

William 'Bill' Wentworth
William 'Bill' Wentworth

Reputation: 1787

This information comes from the Visual Recognition documentation at the following url: https://www.ibm.com/watson/developercloud/doc/visual-recognition/customizing.html

Size limitations
There are size limitations for training calls and data:

  • The service accepts a maximum of 10,000 images or 100 MB per .zip file.
  • The service requires a minimum of 10 images per .zip file.
  • The service accepts a maximum of 256 MB per training call.
  • Minimum recommend size of an image is 32X32 pixels.

Guidelines for good training Anchor link
The following guidelines are not enforced by the API. However, the service tends to perform better when the training data adheres to them:

  • A minimum of 50 images is recommended in each .zip file, as fewer than 50 images can decrease the quality of the trained classifier.
  • If the quality and content of training data is the same, then classifiers that are trained on more images will generally be more accurate than classifiers that are trained on fewer images. The benefits of training a classifier on more images plateaus at around 5000 images, and this can take a while to process. You can train a classifier on more than 5000 images, but it may not significantly increase that classifier's accuracy.
  • Uploading a total of 150-200 images per .zip file gives you the best balance between the time it takes to train and the improvement to classifier accuracy. More than 200 images increases the time, and it does increase the accuracy, but with diminishing returns for the amount of time it takes.
  • Include approximately the same number of images in each examples file. Including an unequal number of images can cause the quality of the trained classifier to decline.
  • The accuracy of your custom classifier can be affected by the kinds of images you provide to train it. Provide example images that are similar to the images you plan to analyze. For example, if you are training the classifier "tiger", your classifier might be less accurate if you provide only images of tigers in a zoo taken by a mobile phone to train the classifier, but you want to test the classifier on images of tigers in the wild taken by professional photographers.

Guidelines for high volume classifying Anchor link

If you want to classify many images, submitting one image at a time can take a long time. You can maximize efficiency and performance of the service in the following ways:

  • Resize images to be no larger than 320 pixels in either width or height. Images do not need to be high resolution.
  • Submit images in batches as compressed (.zip) files.
  • Specify only the classifiers you want results for in the classifier_ids parameter. If you do not specify a value for this parameter, the service classifies the images against the default classifier and takes longer to return a response.

Upvotes: 3

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