Rude Carnie: Age and Gender Deep Learning with TensorFlow
Goal
Do face detection and age and gender classification on pictures
Name
Currently Supported Models
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Gil Levi and Tal Hassner, Age and Gender Classification Using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015
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Inception v3 with fine-tuning
- This will start with an inception v3 checkpoint, and fine-tune for either age or gender detection
Running
There are several ways to use a pre-existing checkpoint to do age or gender classification. By default, the code will simply assume that the image you provided has a face in it, and will run that image through a multi-pass classification using the corners and center.
The --class_type parameter controls which task, and the --model_dir controls which checkpoint to restore. There are advanced parameters for the checkpoint basename (--checkpoint) and the requested step number if there are multiple checkpoints in the directory (--requested_step)
Here is a run using Age classification on the latest checkpoint in a directory using 12-look (all corners + center + resized, along with flipped versions) averaging:
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_1/run-20854 --filename /home/dpressel/Downloads/portraits/prince.jpg
You can also tell it to do a single image classification without the corners and center crop. Here is a run using Age classification on the latest checkpoint in a directory, using a single look at the image
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_1/run-20854 --filename /home/dpressel/Downloads/portraits/prince.jpg --single_look
Here is a version using gender, where we restore the checkpoint from a specific step:
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0/run-31376 --class_type gender --requested_step 9999 --filename /home/dpressel/Downloads/portraits/prince.jpg
Face Detection
If you have an image with one or more frontal faces, you can run a face-detector upfront, and each detected face will be chipped out and run through classification individually. A variety of face detectors are supported including OpenCV, dlib and YOLO
OpenCV:
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename /home/dpressel/Downloads/portraits/p_and_d.jpg --face_detection_model /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml
To use dlib, you will need to install it and grab down the model:
wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
bunzip2 bunzip2 shape_predictor_68_face_landmarks.dat.bz2
pip install dlib
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename ~/Downloads/portraits/halloween15.jpg --face_detection_type dlib --face_detection_model shape_predictor_68_face_landmarks.dat
YOLO tiny:
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename /home/dpressel/Downloads/portraits/p_and_d.jpg --face_detection_model weights/YOLO_tiny.ckpt --face_detection_type yolo_tiny
If you want to run YOLO, get the tiny checkpoint from here
https://github.com/gliese581gg/YOLO_tensorflow/
The YOLO detection code is based heavily on:
https://github.com/gliese581gg/YOLO_tensorflow/blob/master/YOLO_tiny_tf.py
Prediction with fine-tuned inception model
If you want to use guess.py with an inception fine-tuned model, the usage is the same as above, but remember to pass --model_type inception:
$ python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename /home/dpressel/Downloads/portraits/prince.jpg
Here is a gender guess:
$ python guess.py --class_type gender --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/gender/inception/21936/ --filename /home/dpressel/Downloads/portraits/Dan-Pressel-3.png
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.7.5 locally
...
Executing on /cpu:0
selected (fine-tuning) inception model
/data/xdata/rude-carnie/checkpoints/gender/inception/21936/checkpoint-14999
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 980M, pci bus id: 0000:01:00.0)
Running file /home/dpressel/Downloads/portraits/Dan-Pressel-3.png
Converting PNG to JPEG for /home/dpressel/Downloads/portraits/Dan-Pressel-3.png
Running multi-cropped image
Guess @ 1 M, prob = 0.99
Pre-trained Checkpoints
You can find a pre-trained age checkpoint for inception here:
https://drive.google.com/drive/folders/0B8N1oYmGLVGWbDZ4Y21GLWxtV1E
A pre-trained gender checkpoint for inception is available here:
https://drive.google.com/drive/folders/0B8N1oYmGLVGWemZQd3JMOEZvdGs
Training
You can use your own training data if you wish. This is a little easier to do with gender, since there are many ways that you could come up with a training set for this, but it has been developed specifically with the Adience corpus in mind, and uses the pre-splits created by Levi and Hassner.
Download Adience data and folds
The Adience data page is here, where you can download the aligned dataset used in the example usage below:
http://www.openu.ac.il/home/hassner/Adience/data.html
Get the folds, we dont need to run their preprocessing scripts since we are doing this in the preproc.py script using tensorflow
git clone https://github.com/GilLevi/AgeGenderDeepLearning
Pre-process data for training
First you will need to preprocess the data using preproc.py. This assumes that there is a directory that is passed for an absolute directory, as well as a file containing a list of the training data images and the label itself, and the validation data, and test data if applicable. The preproc.py program generates 'shards' for each of the datasets, each containing JPEG encoded RGB images of size 256x256
$ python preproc.py --fold_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/train_val_txt_files_per_fold/test_fold_is_0 --train_list age_train.txt --valid_list age_val.txt --data_dir /data/xdata/age-gender/aligned --output_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_0
The training (etc) lists are expected in the --fold_dir, and they contain first the relative path from the --data_dir and second the numeric label:
dpressel@dpressel:~/dev/work/3csi-rd/dpressel/sh$ head /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/train_val_txt_files_per_fold/test_fold_is_0/age_train.txt
10069023@N00/landmark_aligned_face.1924.10335948845_0d22490234_o.jpg 5
7464014@N04/landmark_aligned_face.961.10109081873_8060c8b0a5_o.jpg 4
28754132@N06/landmark_aligned_face.608.11546494564_2ec3e89568_o.jpg 2
10543088@N02/landmark_aligned_face.662.10044788254_2091a56ec3_o.jpg 3
66870968@N06/landmark_aligned_face.1227.11326221064_32114bf26a_o.jpg 4
7464014@N04/landmark_aligned_face.963.10142314254_8e96a97459_o.jpg 4
113525713@N07/landmark_aligned_face.1016.11784555666_8d43b6c493_o.jpg 3
30872264@N00/landmark_aligned_face.603.9575166089_f5f9cecc8c_o.jpg 5
10897942@N03/landmark_aligned_face.633.10372582914_382144ffe8_o.jpg 3
10792106@N03/landmark_aligned_face.522.11039121906_b047c90cc1_o.jpg 3
Gender is done much the same way:
$ python preproc.py --fold_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/train_val_txt_files_per_fold/test_fold_is_0 --train_list gender_train.txt --valid_list gender_val.txt --data_dir /data/xdata/age-gender/aligned --output_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0
Train the model (Levi/Hassner)
Now that we have generated the training and validation shards, we can start training the program. Here is a simple way to call the driver program to run using SGD with momentum to train:
$ python train.py --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_0
Once again, gender is done much the same way. Just be careful that you are running on the the preprocessed gender data, not the age data. Here we use a lower initial learning rate of 0.001
$ python train.py --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0 --max_steps 30000 --eta 0.001
Train the model (fine-tuned Inception)
Its also easy to use this codebase to fine-tune an pre-trained inception checkpoint for age or gender dectection. Here is an example for how to do this:
$ python train.py --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_0 --max_steps 15000 --model_type inception --batch_size 32 --eta 0.001 --dropout 0.5 --pre_model /data/pre-trained/inception_v3.ckpt
You can get the inception_v3.ckpt like so:
https://github.com/tensorflow/models/tree/master/im2txt#download-the-inception-v3-checkpoint
Monitoring the training
You can easily monitor the job run by launching tensorboard with the --logdir specified in the program's initial output:
tensorboard --logdir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0/run-31376
Then navigate to http://127.0.0.1:6006/ in your browser to see results. The first tab (events) shows the loss over time, and the second shows the images that the network is seeing during training on batches.
Evaluate the model
The evaluation program is written to be run alongside the training or after the fact. If you run it after the fact, you can specify a list of checkpoint steps to evaluate in sequence. If you run while training is working, it will periodically rerun itself on the latest checkpoint.
Here is an example of running evaluation continuously. The --run_id will live in the --train_dir (run-) and is the product of a single run of training (the id is actually the PID used in training):
$ python eval.py --run_id 15918 --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0/ --eval_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/eval_gen_test_fold_is_0
Here is an after-the-fact run of eval that loops over the specified checkpoints and evaluates the performance on each:
$ python eval.py --run_id 25079 --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_0/ --eval_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/eval_age_test_fold_is_0 --requested_step_seq 7000,8000,9000,9999
To monitor the fine-tuning of an inception model, the call is much the same. Just be sure to pass --model_type inception
$ python eval.py --run_id 8128 --train_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_0/ --eval_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/eval_age_test_fold_is_0 --model_type inception