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Diffstat (limited to 'tensorflow/docs_src/tutorials/image_retraining.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/image_retraining.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md index b0e715edcb..90652ac405 100644 --- a/tensorflow/docs_src/tutorials/image_retraining.md +++ b/tensorflow/docs_src/tutorials/image_retraining.md @@ -6,7 +6,7 @@ work by taking a fully-trained model for a set of categories like ImageNet, and retrains from the existing weights for new classes. In this example we'll be retraining the final layer from scratch, while leaving all the others untouched. For more information on the approach you can see -[this paper on Decaf](http://arxiv.org/pdf/1310.1531v1.pdf). +[this paper on Decaf](https://arxiv.org/pdf/1310.1531v1.pdf). Though it's not as good as a full training run, this is surprisingly effective for many applications, and can be run in as little as thirty minutes on a @@ -213,7 +213,7 @@ the object you actually care about. To avoid this, try to take pictures in as wide a variety of situations as you can, at different times, and with different devices. If you want to know more about this problem, you can read about the classic (and possibly apocryphal) -[tank recognition problem](http://www.jefftk.com/p/detecting-tanks). +[tank recognition problem](https://www.jefftk.com/p/detecting-tanks). You may also want to think about the categories you use. It might be worth splitting big categories that cover a lot of different physical forms into |