Emotion Recognition Implemented by ModelZoo.
Firstly, you need to clone this repository and download training data:
git clone https://github.com/ModelZoo/EmotionRecognition.git
cd EmotionRecognition
git lfs pull
Next, install the dependencies using pip:
pip3 install -r requirements.txt
Finally, just run training:
python3 train.py
If you want to continue training your model, you need to define checkpoint_restore flag in train.py:
tf.flags.DEFINE_bool('checkpoint_restore', True, help='Model restore')And you can define the specific model with checkpoint_name which you want to continue training with:
tf.flags.DEFINE_string('checkpoint_name', 'model-178.ckpt', help='Model name')After training, you can see the transition of loss in TensorBoard.
cd events
tensorboard --logdir=.
The best accuracy is 65.64% from step 178.
Next, we can use our model to recognize the emotion.
Here are the test pictures we picked from the website:
Then put them to the folder named tests and define the
model path and test folder in infer.py:
tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-178', help='Model name')
tf.flags.DEFINE_string('test_dir', 'tests/', help='Dir of test data')Then just run inference using this cmd:
python3 infer.py
We can get the result of emotion recognition and probabilities of each emotion:
Image Path: test1.png
Predict Result: Happy
Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 1.0, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.0}
====================
Image Path: test2.png
Predict Result: Happy
Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.998, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.002}
====================
Image Path: test3.png
Predict Result: Surprise
Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.0, 'Sad': 0.0, 'Surprise': 1.0, 'Neutral': 0.0}
====================
Image Path: test4.png
Predict Result: Angry
Emotion Distribution: {'Angry': 1.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.0, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.0}
====================
Image Path: test5.png
Predict Result: Fear
Emotion Distribution: {'Angry': 0.04, 'Disgust': 0.002, 'Fear': 0.544, 'Happy': 0.03, 'Sad': 0.036, 'Surprise': 0.31, 'Neutral': 0.039}
====================
Image Path: test6.png
Predict Result: Sad
Emotion Distribution: {'Angry': 0.005, 'Disgust': 0.0, 'Fear': 0.027, 'Happy': 0.002, 'Sad': 0.956, 'Surprise': 0.0, 'Neutral': 0.009}
Emmm, looks good!
Looking for pretrained model?
just go to checkpoints folder, here is the model with best performance at step 178.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent