wert23239 / face-orientation

Face Orientation Resnet Project

Description

A trained classifier that is able to detect face orientation. The model is based off a pre-trained resnet model that is transferred over. It is able to detect up and down very well but struggles a slight amount with right and left. This model could be improved further with more precise fine tuning.

Results

Face Orientation Classifier

	Up	Down	Left	Right	Total
Train Accuracy	100%	100%	96.8%	90%	96.8%
Validation Accuracy	96.8%	100%	93.8%	81.2%	93%

Instructions

1. Download the CelebA Database. This should be saved as "img_align_celeba".
2. Run the ImageProcessor.py to convert the data into the correct size and folders. Note you will need to create the "processedImages/" folders. This includes a train and validate folder with each label "up" , "down", "left" , and "right". See Command #1 for help.
3. clone the GitHub repo for Tensorflow Models and follow the instructions in the official folder README. This should be in the same folder as this repo.
4. Run the build_image_data.py command using Command #2. The "tfRecords/" will also have to be created. With the latest Tensorflow binaries you will need to add the following lines to line 77-78. import tensorflow.compat.v1 as tf tf.disable_eager_execution()
5. Download the latest model from the official/r1/resnet/ README. Rename this to "PreResNet/".
6. Finally you can run the code using Command #3. You will need to make the following changes.
   1. Line 714 of resnet_run_loop.py change to classifier.export_saved_model("results/", input_receiver_fn)
   2. The final line of imagenet_preprocessing.py change to image = tf.image.decode_image(image_buffer, channels=num_channels , expand_animations=False) image.set_shape([output_height, output_width, num_channels])
7. Explore the data using the Predict.ipynb

Commands

1. mkdir -p processedImages/train/up && mkdir -p processedImages/train/down && mkdir -p processedImages/train/left && mkdir -p processedImages/train/right && mkdir -p processedImages/validate/up && mkdir -p processedImages/validate/down && mkdir -p processedImages/validate/left && mkdir -p processedImages/validate/right
2. python models/research/inception/inception/data/build_image_data.py --train_directory processedImages/train/ --validation_directory processedImages/validate/ --output_directory tfrecords/ --labels_file labels.txt --train_shards 1024 --validation_shards 128
3. python models/official/r1/resnet/imagenet_main.py --data_dir tfrecords/ --pretrained_model_checkpoint_path PreResNet --fine_tune False --rv 2 --export_dir results/ --train_epoch 10