Object Detection Using Deep CNNs Trained on Synthetic Images

Understand

The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications.

• We show that with transfer learning an effective object detector can be trained almost entirely on synthetically rendered datasets.
• We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes.
• Our CNN trained only with 4000 synthetic images achieves mean average precision (mAP) of 24 on a test set with 55 distinct products as objects of interest and 17 distractor objects.