5/1/2023 0 Comments Finetune googlenet caffeRecent work has shown that translation can be done in a much simpler way using RNNs and outperform the state-of-the-art performance. The main inspiration of our work comes from recent advances in machine translation, where the task is to translate words or sentences individually. and modify the model for optimizing fashion classification for the purposes of annotating images and predicting clothing tags for the fashion images. Depending on the application of fashion classification, the most relevant problems to solve will differ. shows that real-time clothing recognition can be useful in the surveillance context, where information about individuals’ clothes can be used to identify crime suspects. For example Liu et.al.’s work on predicting the clothing details in an unlabeled image can facilitate the discovery of the most similar fashion items in an e-commerce database. Given the role of clothing apparel in society, fashion classification has many applications. Please feel free to email me at you have questions. You can register for live webinar to learn more on Microsoft Azure and Deep Learning. I am delivering a Deep Learning webinar on 27 th September 2016, 10:00am-11:00am PST. In this blog, I show re-usability of trained DCNN model by combining it with a Long Short-term Memory (LSTM) Recurrent Neural Network (RNN). This Part 3 of the series is based on my talk at PAPI 2016. In Part 2, I describe Deep Convolutional Neural Network (DCNN) and how Transfer learning and Fine-tuning helps better the training process for domain specific images. A very recent benchmarking paper compares CNTK with Caffe, Torch & TensorFlow, and CNTK performs substantially better than all the other three frameworks. ![]() In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. This is part 3 of my series on Deep Learning, where I describe my experiences and go deep into the reasons behind my choices. ![]() By Anusua Trivedi, Microsoft Data Scientist
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