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 # NeuralTalk2
 
-**Update (September 22, 2016)**: The Google Brain team has [released the image captioning model](https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html) of Vinyals et al. (2015). The core model is very similar to NeuralTalk2 (a CNN followed by RNN), but the Google release should work significantly better as a result of better CNN, some tricks, and more careful engineering. Find it under [im2txt](https://github.com/tensorflow/models/tree/master/im2txt/im2txt) repo in tensorflow. I'll leave this code base up for educational purposes and as a Torch implementation.
+**Update (September 22, 2016)**: The Google Brain team has [released the image captioning model](https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html) of Vinyals et al. (2015). The core model is very similar to NeuralTalk2 (a CNN followed by RNN), but the Google release should work significantly better as a result of better CNN, some tricks, and more careful engineering. Find it under [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) repo in tensorflow. I'll leave this code base up for educational purposes and as a Torch implementation.
 
 Recurrent Neural Network captions your images. Now much faster and better than the original [NeuralTalk](https://github.com/karpathy/neuraltalk). Compared to the original NeuralTalk this implementation is **batched, uses Torch, runs on a GPU, and supports CNN finetuning**. All of these together result in quite a large increase in training speed for the Language Model (~100x), but overall not as much because we also have to forward a VGGNet. However, overall very good models can be trained in 2-3 days, and they show a much better performance.