Introduction to Image Captioning

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Image Captioning is the process of using a deep learning model to  describe the content of an image. Most captioning architectures use an  encoder-decoder framework, where a convolutional neural network (CNN)  encodes the visual features of an image, and a recurrent neural network  (RNN) decodes the features into a descriptive text sequence.

VQA

Visual Question Answering (VQA) is the process of asking a question  about the contents of an image, and outputting an answer. VQA uses  similar architectures to image captioning, except that a text input is  also encoded into the same vector space as the image input.

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Image captioning and VQA are used in a wide array of applications:

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Why Captioning With fastdup?

Image captioning can be a computationally-expensive task, requiring many processor hours to conduct. Recent experiments have shown that the free fastdup tool can be used to reduce dataset  size without losing training accuracy. By generating captions and VQAs  with fastdup, you can save expensive compute hours by filtering out  duplicate data and unnecessary inputs.

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Getting Started With Captioning in fastdup

To start generating captions with fastdup, you’ll first need to install and import fastdup in your computing environment.

Processor Selection and Batching

The  captioning method in fastdup enables you to select either a GPU or CPU  for computation, and decide your preferred batch size. By default, CPU  computation is selected, and batch sizes are set to 8. For GPUs with  high-RAM (40GB), a batch size of 256 will enable captioning in under  0.05 seconds per image.

To select a model, processing device, and batch size, the following syntax is used. If no parameters are entered, the fd.caption() method will default to ViT-GPT2, CPU processing, and a batch size of 8.

“The  captioning method in fastdup enables you to select either a GPU or CPU  for computation, and decide your preferred batch size. By default, CPU  computation is selected, and batch sizes are set to 8. For GPUs with  high-RAM (40GB), a batch size of 256 will enable captioning in under  0.05 seconds per image.”
Dean Scontras, AVP, Public Sector, Wiz

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Image captioning and VQA are used in a wide array of applications:

• ⚡ Quickstart:  Learn how to install fastdup, load a dataset, and analyze it for  potential issues such as duplicates/near-duplicates, broken images,  outliers, dark/bright/blurry images, and view visually similar image  clusters. If you’re new, start here!
• 🧹 Clean Image Folder:  Learn how to analyze and clean a folder of images from potential issues  and export a list of problematic files for further action. If you have  an unorganized folder of images, this is a good place to start.
• 🖼 Analyze Image Classification Dataset:  Learn how to load a labeled image classification dataset and analyze  for potential issues. If you have labeled ImageNet-style folder  structure, have a go!
• 🎁 Analyze Object Detection Dataset:  Learn how to load bounding box annotations for object detection and  analyze for potential issues. If you have a COCO-style labeled object  detection dataset, give this example a try.