Can we train a neural network to tell if an object is present or not in an Image?
A standard computer vision and deep learning dataset for this problem was developed by the Canadian Institute for Advanced Research (CIFAR). The CIFAR-10 dataset consists of 60,000 photos divided into 10 classes (hence the name CIFAR-10). Classes include common objects such as airplanes, automobiles, birds, cats and so on. The dataset is split in a standard way, where 50,000 images are used for training a model and the remaining 10,000 for evaluating its performance. Other datasets are MNIST, CIFAR-100 STL-10, SVHN, ILSVRC2012 etc

State of the art results are achieved using very large Convolutional Neural networks. You can learn about state of the are results on CIFAR-10 on Rodrigo Benenson’s webpage. Model performance is reported in classification accuracy, with very good performance above 90%.

Here is a sample tutorial on convolutional neural network with caffe and python