In the part-1 of this two-part blog series, a list of object detection datasets were presented. In this second part, a list of image classification type datasets is provided along with training and inferencing codes.

An object recognition system involves localizing an object of interest and then tagging it with a label. An image classification system can be considered as an application that attaches single or multiple tags to an image, for example,

• Analysing a pic is of a dog or a cat

• Distinguishing a cancerous cell from a normal one

• Attaching multiple tags based on daylight time (day, night, evening), scene type (indoor, garden area, on-road), quality of the image, etc

One tackle an object recognition problem using complex algorithms such as SSD, EfficientDet, Mask-Rcnn, Yolo, Retinanet, etc. Whereas while taking on an image classification challenge you depend more on neural network (CNNs most of the time) architecture such as Densenets, Resnets, Mobinets, Vgg-nets, etc. You may approach the training using transfer learning where you pre-train your model on a large dataset so that it learns how to extract important features from an image. Or, you design your own network and train it from scratch.

And as mentioned in the blog-1 as well, it is really important to test your theoretical knowledge on datasets from different domains. The way you handle medical imaging dataset tends to differ from the way you handle a dataset of fashion products.

Our opensource team at Monk Computer Vision Org compiled this list of image classification datasets and created short tutorials over each of them for you to utilize these datasets and try out different transfer learning experiments with varied hyperparameters

In this blog, datasets from following industries are listed

★ Art

★ Agriculture

★ Automobile and Advanced Driver Assistance Systems

★ Fashion

★ Food and Groceries

★ Wildlife

★ Sports

★ Satellite Imaging

★ Medical Imaging and Healthcare

★ Security and Surveillance

★ Scene type understanding

…… and much more!!!

The complete list at one place is available on Github with associated usage instructions and training codes

#industry #computer-vision #image-classification #deep-learning