This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to “slow” R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at this https URL
DIGITS 2 ile Tanışın
NVIDIA’nın kullanıma sunduğu derin öğrenme algoritmalarını çalıştırabileceğiniz uygulamanın yani DIGITS’in ikinci sürümü bir süre önce duyurulmuştu. NVIDIA DIGITS sunduğu web arayüzü ile kendi resimlerinizi hızlı ve kolay bir şekilde eğitmenize imkan sağlarken derin öğrenme algoritmanızın görselleştirilmesi, parametrelerin düzenlenmesini kolay bir hale getirmiştir. Aşağıda 60 dakikalık giriş dersi videosu ve yansıları faydalanmanız için sunulmuştur.
Tez: Improving Neural Networks with Dropout
| Info |
|---|
| Nitish Srivastava |
| Master’s Thesis |
| 2013 |
| University of Toronto |
Deep neural nets with a huge number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from a neural network during training. This prevents the units from co-adapting too much. Dropping units creates thinned networks during training. The number of possible thinned networks is exponential in the number of units in the network. At test time all possible thinned networks are combined using an approximate model averaging procedure. Dropout training followed by this approximate model combination significantly reduces overfitting and gives major improvements over other regularization methods. In this work, we describe models that improve the performance of neural networks using dropout, often obtaining state-of-the-art results on benchmark datasets.
Makale: Real-Time Pedestrian Detection With Deep Networks Cascades
We present a new real-time approach to object detection that exploits the efficiency of cascade classifiers with the accuracy of deep neural networks. Deep networks have been shown to excel at classification tasks, and their ability to operate on raw pixel input without the need to design special features is very appealing. However, deep nets are notoriously slow at inference time. In this paper, we propose an approach that cascades deep nets and fast features, that is both extremely fast and extremely accurate. We apply it to the challenging task of pedestrian detection. Our algorithm runs in real-time at 15 frames per second. The resulting approach achieves a 26.2% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the very best reported results. It is the first work we are aware of that achieves extremely high accuracy while running in real-time.