Teaching Assistant


  • CNN
  • Object Detection
  • Image Classification

Abstract: Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224×224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should, in general, improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is24-102×faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007.

Conference: ECCV 2014

Dataset: VOC 2007/ ILSVRC 2014


  1. Implement the model on Tensorflow / Keras
  2. Implement the proper data loader with any 2 data augmentation techniques.
  3. Use subsets of the dataset for paper implementation(in case of out of memory). Clearly report the number of data samples of each category in training and testing sets.