1614097070
CMS Face-off: Drupal Vs WordPress
Are you indecisive about which content management system (CMS) to choose for your business? If yes, you’re not alone. When it comes to choosing an open-source CMS, you’ll be spoilt for choice. Of all the content management systems out there, Drupal and WordPress are the popular ones. Both the platforms come with a host of features and functionalities that let developers build feature-rich and scalable websites catering to their clients’ unique requirements. Now, let’s dive deep into how Drupal and WordPress are different from each other and which one you should choose for your business in 2021. Let’s get started.
Drupal
Drupal is not a newbie in the market. It has been around since 15 January 2001. This free and open-source CMS is trusted by the top brands across the world including Tesla, Verizon, eBay, Twitter, Timex, Nokia, Harvard University, Stanford University, NASA, and the list goes on. Unlike WordPress that is primarily used for blogging, Drupal is used for enterprise-grade projects that need to be scalable in the future. A Drupal web development company can leverage this platform to build professional websites, blogs, social networking sites, forums, and more.
Top Features of Drupal CMS
- Great Accessibility Support
- Multilingual Support
- Mobile-friendly Themes
- HTML5 Prowess
- Doesn’t require any licensing
- Compatible with eCommerce, CRM, APIs modules
- Acclaimed for its strong security, and timely updates
- 30000+ modules that allow you to add any feature to your website
WordPress
Launched back on 27 May 2003, WordPress has become synonymous with both blogging and content management systems. In fact, you would be astonished to know that today WordPress is driving 40% of the web and is trusted by the world’s leading brands including TechCrunch, Sony Music, Bata, PlayStation Blog, The Walt Disney Company, Angry Birds, Time Inc., Marks & Spencer for Business, Variety, and many more. This powerful CMS is not only trusted by Fortune 500 companies but also by millions of bloggers, small businesses, etc.
Top Features of Using WordPress CMS
- Search engine optimization (SEO) friendly
- Easy to use (even newbies can use it without hassle)
- WordPress powered sites are mobile responsive
- Strong community support
- Support a wide gamut of media types
- Provide you full control of your website
- A plethora of plugins to extend your site functionality
- Give you the freedom to choose the hosting of your choice
Final Words
Both Drupal and WordPress have their own perks, however, choosing the right CMS depends upon your business requirements. For instance, if you need a simple site that you can easily manage without having technical know-how, it’s wise choosing WordPress. On the other hand, if you have a large website that is a bit complex, it’s recommended to choose Drupal CMS. If you don’t have sound technical knowledge, make sure you seek help from a professional Drupal development company when embracing this content management system (CMS). Hope this informative piece helps you decide which CMS you should opt for your business in 2021. Besides choosing the right platform, you should also make sure you partner with a trusted company to make the most of the content management system you choose.
#drupal website development company #drupal development company #drupal web development company
What is GEEK
Buddha Community
1641812160
A Lightweight Face Recognition and Facial Attribute Analysis
deepface
Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
Installation
The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly based on TensorFlow and Keras.
pip install deepfaceThen you will be able to import the library and use its functionalities.
from deepface import DeepFaceFacial Recognition - Demo
A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.
Face Verification - Demo
This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
Face recognition - Demo
Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
Face recognition models - Demo
Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.
models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])
#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.
| Model | LFW Score | YTF Score |
|---|---|---|
| Facenet512 | 99.65% | - |
| ArcFace | 99.41% | - |
| Dlib | 99.38 % | - |
| Facenet | 99.20% | - |
| VGG-Face | 98.78% | 97.40% |
| Human-beings | 97.53% | - |
| OpenFace | 93.80% | - |
| DeepID | - | 97.05% |
Similarity
Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.
Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.
metrics = ["cosine", "euclidean", "euclidean_l2"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])
#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])
Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.
Facial Attribute Analysis - Demo
Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.
obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])
Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.
Streaming and Real Time Analysis - Demo
You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.
DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.
user
├── database
│ ├── Alice
│ │ ├── Alice1.jpg
│ │ ├── Alice2.jpg
│ ├── Bob
│ │ ├── Bob.jpg
Face Detectors - Demo
Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN and RetinaFace detectors are wrapped in deepface.
All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])
#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])
#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])
#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])
Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.
RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.
The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.
You can find out more about RetinaFace on this repo.
API - Demo
Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.
python api.py
Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.
Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.
embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')
Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.
Contribution
Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.
Support
There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏
You can also support this work on Patreon
Citation
Please cite deepface in your publications if it helps your research. Here are its BibTeX entries:
@inproceedings{serengil2020lightface,
title = {LightFace: A Hybrid Deep Face Recognition Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages = {23-27},
year = {2020},
doi = {10.1109/ASYU50717.2020.9259802},
url = {https://doi.org/10.1109/ASYU50717.2020.9259802},
organization = {IEEE}
}
@inproceedings{serengil2021lightface,
title = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
pages = {1-4},
year = {2021},
doi = {10.1109/ICEET53442.2021.9659697},
url. = {https://doi.org/10.1109/ICEET53442.2021.9659697},
organization = {IEEE}
}
Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.
Author: Serengil
Source Code: https://github.com/serengil/deepface
License: MIT License
1648217849
Deepface: A Face Recognition and Facial Attribute Analysis for Python
deepface
Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
Installation
The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly powered by TensorFlow and Keras.
pip install deepface
Then you will be able to import the library and use its functionalities.
from deepface import DeepFace
Facial Recognition - Demo
A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.
Face Verification - Demo
This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
Face recognition - Demo
Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
Face recognition models - Demo
Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.
models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])
#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.
| Model | LFW Score | YTF Score |
|---|---|---|
| Facenet512 | 99.65% | - |
| ArcFace | 99.41% | - |
| Dlib | 99.38 % | - |
| Facenet | 99.20% | - |
| VGG-Face | 98.78% | 97.40% |
| Human-beings | 97.53% | - |
| OpenFace | 93.80% | - |
| DeepID | - | 97.05% |
Similarity
Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.
Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.
metrics = ["cosine", "euclidean", "euclidean_l2"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])
#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])
Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.
Facial Attribute Analysis - Demo
Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.
obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])
Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.
Streaming and Real Time Analysis - Demo
You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.
DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.
user
├── database
│ ├── Alice
│ │ ├── Alice1.jpg
│ │ ├── Alice2.jpg
│ ├── Bob
│ │ ├── Bob.jpg
Face Detectors - Demo
Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN, RetinaFace and MediaPipe detectors are wrapped in deepface.
All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']
#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])
#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])
#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])
#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])
Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.
RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.
The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.
You can find out more about RetinaFace on this repo.
API - Demo
Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.
python api.py
Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.
Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.
embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')
Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.
Contribution
Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.
Support
There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏
You can also support this work on Patreon
Citation
Please cite deepface in your publications if it helps your research. Here are BibTeX entries:
@inproceedings{serengil2020lightface,
title = {LightFace: A Hybrid Deep Face Recognition Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages = {23-27},
year = {2020},
doi = {10.1109/ASYU50717.2020.9259802},
url = {https://doi.org/10.1109/ASYU50717.2020.9259802},
organization = {IEEE}
}
@inproceedings{serengil2021lightface,
title = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
booktitle = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
pages = {1-4},
year = {2021},
doi = {10.1109/ICEET53442.2021.9659697},
url = {https://doi.org/10.1109/ICEET53442.2021.9659697},
organization = {IEEE}
}
Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.
Download Details:
Author: serengil
Source Code: https://github.com/serengil/deepface
License: MIT License
1614097070
CMS Face-off: Drupal Vs WordPress
Are you indecisive about which content management system (CMS) to choose for your business? If yes, you’re not alone. When it comes to choosing an open-source CMS, you’ll be spoilt for choice. Of all the content management systems out there, Drupal and WordPress are the popular ones. Both the platforms come with a host of features and functionalities that let developers build feature-rich and scalable websites catering to their clients’ unique requirements. Now, let’s dive deep into how Drupal and WordPress are different from each other and which one you should choose for your business in 2021. Let’s get started.
Drupal
Drupal is not a newbie in the market. It has been around since 15 January 2001. This free and open-source CMS is trusted by the top brands across the world including Tesla, Verizon, eBay, Twitter, Timex, Nokia, Harvard University, Stanford University, NASA, and the list goes on. Unlike WordPress that is primarily used for blogging, Drupal is used for enterprise-grade projects that need to be scalable in the future. A Drupal web development company can leverage this platform to build professional websites, blogs, social networking sites, forums, and more.
Top Features of Drupal CMS
- Great Accessibility Support
- Multilingual Support
- Mobile-friendly Themes
- HTML5 Prowess
- Doesn’t require any licensing
- Compatible with eCommerce, CRM, APIs modules
- Acclaimed for its strong security, and timely updates
- 30000+ modules that allow you to add any feature to your website
WordPress
Launched back on 27 May 2003, WordPress has become synonymous with both blogging and content management systems. In fact, you would be astonished to know that today WordPress is driving 40% of the web and is trusted by the world’s leading brands including TechCrunch, Sony Music, Bata, PlayStation Blog, The Walt Disney Company, Angry Birds, Time Inc., Marks & Spencer for Business, Variety, and many more. This powerful CMS is not only trusted by Fortune 500 companies but also by millions of bloggers, small businesses, etc.
Top Features of Using WordPress CMS
- Search engine optimization (SEO) friendly
- Easy to use (even newbies can use it without hassle)
- WordPress powered sites are mobile responsive
- Strong community support
- Support a wide gamut of media types
- Provide you full control of your website
- A plethora of plugins to extend your site functionality
- Give you the freedom to choose the hosting of your choice
Final Words
Both Drupal and WordPress have their own perks, however, choosing the right CMS depends upon your business requirements. For instance, if you need a simple site that you can easily manage without having technical know-how, it’s wise choosing WordPress. On the other hand, if you have a large website that is a bit complex, it’s recommended to choose Drupal CMS. If you don’t have sound technical knowledge, make sure you seek help from a professional Drupal development company when embracing this content management system (CMS). Hope this informative piece helps you decide which CMS you should opt for your business in 2021. Besides choosing the right platform, you should also make sure you partner with a trusted company to make the most of the content management system you choose.
#drupal website development company #drupal development company #drupal web development company
1621916889
Wordpress Development India, Hire Wordpress Developers
Hire WordPress developers from IndianAppDevelopers on an hourly or full-time basis to build advanced custom WordPress applications. Our WordPress developers have 5+ years of experience building websites, themes and plugins for small- and large-scale businesses.
You can hire highly knowledgeable WordPress developers in India from us to maintain and deliver the highest quality standards on-time solutions.
Looking to outsource a WordPress development project? Or want to hire WordPress developers? Then, get in touch with us.
#wordpress development india #hire wordpress developers india #wordpress development #wordpress developers #wordpress programmers #hire wordpress programmers
1624519148
Top 6 Alternatives To Hugging Face
- With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead.
In recent news, US-based NLP startup, Hugging Face has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .
Watson Assistant
LUIS:
Lex
Dialogflow
#opinions #alternatives to hugging face #chatbot #hugging face #hugging face ai #hugging face chatbot #hugging face gpt-2 #hugging face nlp #hugging face transformer #ibm watson #nlp ai #nlp models #transformers