- Face Classification with DeepFace library.
- Read 200 images from 4 different actors, use
ArcFace
model to get Feature vectors of them and save in a csv file.
- Create MLP with tensorflow and fit data.
Algorithm |
MLP |
Accuracy |
80% |
|
Accuracy |
|
|
|
Dataset |
MLP |
MLP TF tutorial |
CNN+MLP SGD |
CNN+MLP Adam |
Mnist |
86.33% |
97.45% |
91.24% |
99.03% |
Fashion Mnist |
75.98% |
86.87% |
71.66% |
89.49% |
cifar 10 |
41.15% |
--- |
--- |
70.07% |
cifar 100 |
20.66% |
--- |
--- |
35.38% |
usage: interfrence.py [--input INPUT] [--model MODEL]
- The database consists of 4 categories of images. you can view it here.
- Using VGG16 model.
- Classes:
- Normal people 👨🏻
- Sheikh 👳🏻♂️
VGG16 |
Loss |
Accuracy |
Train |
0.16 |
92.45% |
Validation |
0.19 |
96.15% |
Test |
0.28 |
95.83% |
- Confusion Matrix:
![confmat](https://user-images.githubusercontent.com/77120507/158569528-8188f50f-c95f-452d-9f2d-b16e09c4783d.png)
- Face Mask Detection using Tensorflow Keras, PySide6, open-cv.
- Dataset: Images Dataset
- Model:
|
Accuracy |
Loss |
MobileNetV2 |
99.29% |
0.02 |
facemask.MP4
- Dataset: 17 category flowers
- 17 flowers Classification using Tensorflow Keras.
- Model:
- Resnet50V2
- Xception
- InceptionResNetV2
|
Accuracy |
Loss |
ResNet50V2 |
84.19% |
0.51 |
Xception |
81.99% |
0.61 |
InceptionResNetV2 |
75.37% |
0.75 |
-
Dataset: Link
-
usage:
pip install -r requirements.txt
python cnn_regression.py -d HousesDataset
-
inference:
- Dataset: utkface
- Estimating human age using Tensorflow Keras.
- Model:
|
Loss(mse) |
Xception |
124.68 |
Resnet50V2 |
145.41 |
- Face Recognition exercise using Tensorflow.
|
Accuracy |
Loss |
Model |
84% |
0.02 |
-
Inference:
usage: python inference.py [image PATH] [weight PATH]