Smart Crowd Analyzer is a product which can enhance your Managing experience. Field of area we implied our product is Shopping Mall. Our Product can:
Brochure is Uploaded in files
Datasets were taken from:
Age and Gender Detection Dataset: https://www.kaggle.com/jangedoo/utkface-new?
Data was Uploaded , Trained , preprocesed and then Evaluated. The accuracy we got was 60-70% for Age Detection . and 80% for Gender detection by this Model.
The Code is under: Age_and_Gender_Prediction.ipynb
Dataset was taken from:
Emotion Detection Dataset: https://www.kaggle.com/shawon10/ckplus
Data was Uploaded , Trained , preprocesed and then Evaluated. The accuracy we got was 100% training accuracy and 100% validation accuracy.
Note: Whenever a model gives a 100% accuracy on test data, we need to check the training accuracy, if that is also 100%. It means the model is actually overfitting and the test set is having a very close distribution to the train set. So, it is showing great results. I think in these circumstances, it’s better to use cross-validation to get the correct intuition of how the model actually works
The Code is under: Emotion_Detection.ipynb
To improve it accuracy wise , what we adopted is DEEPFACE METHOD.
For Colab : Deepface.ipynb is uploaded.
If you're working on a Local Machine, install the dependencies from your terminal with -
pip install -r requirements.txt
Run -Download the Repository. -Install the Dependencies in your system through your terminal/cmd using - -pip install -r requirements. -Open DeepFace.ipynb using any ipynb based console. (Eg- Colab, Jupyter Lab etc)
run the following command in your Terminal/cmd - python deep_face.py
-absl-py==0.9.0 -astunparse==1.6.3 -cachetools==4.1.0 -certifi==2020.4.5.1 -chardet==3.0.4 -click==7.1.2 -cv==1.0.0 -deepface==0.0.24 -filelock==3.0.12 -Flask==1.1.2 -gast==0.3.3 -gdown==3.11.0 -google-auth==1.16.1 -google-auth-oauthlib==0.4.1 -google-pasta==0.2.0 -grpcio==1.29.0 -h5py==2.10.0 -idna==2.9 -itsdangerous==1.1.0 -Jinja2==2.11.2 -Keras==2.3.1 -Keras-Applications==1.0.8 -Keras-Preprocessing==1.1.2 -Markdown==3.2.2 -MarkupSafe==1.1.1 -numpy==1.18.5 -oauthlib==3.1.0 -opencv-python==4.2.0.34 -opt-einsum==3.2.1 -pandas==1.0.4 -Pillow==7.1.2 -protobuf==3.12.2 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 -PySocks==1.7.1 -python-dateutil==2.8.1 -pytz==2020.1 -PyYAML==5.3.1 -requests==2.23.0 -requests-oauthlib==1.3.0 -rsa==4.0 -scipy==1.4.1 -six==1.15.0 -tensorboard==2.2.2 -tensorboard-plugin-wit==1.6.0.post3 -tensorflow==2.2.0 -tensorflow-estimator==2.2.0 -termcolor==1.1.0 -tqdm==4.46.1 -urllib3==1.25.9 -Werkzeug==1.0.1 -wrapt==1.12.1
MIT