https://github.com/modelzoo/facescoring
Face Scoring Model to Get the Beauty Score of Face
https://github.com/modelzoo/facescoring
Last synced: about 1 month ago
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Face Scoring Model to Get the Beauty Score of Face
- Host: GitHub
- URL: https://github.com/modelzoo/facescoring
- Owner: ModelZoo
- Created: 2018-11-03T18:09:09.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-18T18:15:19.000Z (over 6 years ago)
- Last Synced: 2025-03-28T01:48:56.380Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 552 KB
- Stars: 24
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Emotion Recognition
Emotion Recognition Implemented by [ModelZoo](https://github.com/ModelZoo/ModelZoo).
## Usage
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`:
```python
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:
```python
tf.flags.DEFINE_string('checkpoint_name', 'model-178.ckpt', help='Model name')
```
## TensorBoard
After training, you can see the transition of loss in TensorBoard.
```
cd events
tensorboard --logdir=.
```
The best accuracy is 65.64% from step 178.
## Predict
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`:
```python
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!
## Pretrained Model
Looking for pretrained model?
just go to `checkpoints` folder, here is the model with best performance at step 178.