91 lines
3.6 KiB
Markdown
91 lines
3.6 KiB
Markdown
# Training new neural network models
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We have also released our deep neural network (DNN)
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training infrastructure to promote an open ecosystem and enable quicker
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bootstrapping for new research and development.
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There is a distinction between training the DNN model for feature representation
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and training a model for classifying people with the DNN model.
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If you're interested in creating a new classifier,
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see [Demo 3](http://cmusatyalab.github.io/openface/demo-3-classifier/).
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This page is for advanced users interested in training a new DNN model
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and should be done with large datasets (>500k images) to improve the
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feature representation.
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*Warning:* Training is computationally and memory expensive and takes a
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day on our Tesla K40 GPU.
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A rough overview of training is:
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## 1. Create raw image directory.
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Create a directory for your raw images so that images from different
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people are in different subdirectories. The names of the labels or
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images do not matter, and each person can have a different amount of images.
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The images should be formatted as `jpg` or `png` and have
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a lowercase extension.
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```
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$ tree data/mydataset/raw
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person-1
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├── image-1.jpg
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├── image-2.png
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...
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└── image-p.png
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...
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person-m
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├── image-1.png
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├── image-2.jpg
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...
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└── image-q.png
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```
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## 2. Preprocess the raw images
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If you plan to compute LFW accuracies, remove all LFW identities for your dataset.
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We provide an example script doing this with string matching in
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[remove-lfw-names.py](https://github.com/cmusatyalab/openface/blob/master/data/casia-facescrub/remove-lfw-names.py).
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Change `8` to however many
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separate processes you want to run:
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`for N in {1..8}; do ./util/align-dlib.py <path-to-raw-data> align outerEyesAndNose <path-to-aligned-data> --size 96 & done`.
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Prune out directories with less than 3 images per class with
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`./util/prune-dataset.py <path-to-aligned-data> --numImagesThreshold 3`.
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<!-- Split the dataset into `train` and `val` subdirectories -->
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<!-- with `./util/create-train-val-split.py <path-to-aligned-data> <validation-ratio>`. -->
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<!-- One option could be to have all of your data in `train` and -->
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<!-- then validate the model with the LFW experiment. -->
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## 3. Train the model
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Run [training/main.lua](https://github.com/cmusatyalab/openface/blob/master/training/main.lua) to start training the model.
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Edit the dataset options in [training/opts.lua](https://github.com/cmusatyalab/openface/blob/master/training/opts.lua) or
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pass them as command-line parameters.
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This will output the loss and in-progress models to `training/work`.
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The GPU memory usage is determined by the `-peoplePerBatch` and
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`-imagesPerPerson` parameters, which default to 15 and 20 respectively
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and consume about 12GB of memory.
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These determine an upper-bound on the mini-batch size and
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should be reduced for less GPU memory consumption.
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Warning: Metadata about the on-disk data is cached in
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`training/work/trainCache.t7` and assumes
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the data directory does not change.
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If your data directory changes, delete these
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files so they will be regenerated.
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### Stopping and starting training
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Models are saved in the `work` directory after every epoch.
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If the training process is killed, it can be resumed from
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the last saved model with the `-retrain` option.
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Also pass a different `-manualSeed` so a different image
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sequence is sampled and correctly set `-epochNumber`.
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## 4. Analyze training
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Visualize the loss with [training/plot-loss.py](https://github.com/cmusatyalab/openface/blob/master/training/plot-loss.py).
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Install the Python dependencies from
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[training/requirements.txt](https://github.com/cmusatyalab/openface/blob/master/training/requirements.txt)
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with `pip2 install -r requirements.txt`.
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