github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-07 12:05:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

reorganize sections

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2021-06-10 11:22:04 +03:00 committed by GitHub
parent c7641fbec2
commit e7f05fa041
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 22 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
README.md
View File

@ -78,28 +78,6 @@ for metric in metrics:
Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than cosine and regular Euclidean distance based on experiments. Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than cosine and regular Euclidean distance based on experiments.
**Tech Stack** - [`Vlog`](https://youtu.be/R8fHsL7u3eE)
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/tech-stack.png" width="90%" height="90%"></p>
The question is that where to store facial representations. You can find vector embeddings of facial images with the represent function.
```python
embedding = DeepFace.represent("img.jpg", model_name = 'Facenet')
```
Recommended tech stack for face verification is mainly based on [relational databases and regular SQL](https://sefiks.com/2021/02/06/deep-face-recognition-with-sql/) or key-value stores such as [Redis](https://sefiks.com/2021/03/02/deep-face-recognition-with-redis/) or [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/). Herein, key-value stores overperform than regular relational databases.
Face verification is a subset of face recognition. In other words, you can run any face verification tool for face recognition as well. However, face verification has O(1) and face recognition has O(n) time complexity. That's why, face recognition becomes problematic with regular face verification tools on millions/billions level data and limited hardware.
You should use some big data solutions in face recognition when the data becomes huge. NoSQL databases comes with the power of map reduce technology. Here, [Hadoop](https://sefiks.com/2021/01/31/deep-face-recognition-with-hadoop-and-spark/) with Spark or Hive will overperform if you have lots of datanodes and clusters. Besides, [mongoDb](https://sefiks.com/2021/01/22/deep-face-recognition-with-mongodb/) is a document database and it is highly scalable as well.
On the other hand, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalability feature. You should run deepface within those a-nn frameworks if you have really large scale data sets. Those libraries come with high speed but they don't guarantee to find the closest ones always in contrast to k-nn algorithm run in nosql databases.
Finally, graph databases offer to discover relations hard to find. [Neo4j](https://sefiks.com/2021/04/03/deep-face-recognition-with-neo4j/) is a pretty graph database exploring indirect relations between facial images. If you plan to apply unsupervised learning and clustering, then graph databases will work well.
Here, you can find some implementation demos of deepface with a-nn libraries: [`Elasticsearch`](https://youtu.be/i4GvuOmzKzo) and [`Spotify Annoy`](https://youtu.be/Jpxm914o2xk); key-value stores: [`Redis`](https://youtu.be/eo-fTv4eYzo), [`Cassandra`](https://youtu.be/VQqHs6-4Ylg); and graph databases: [`Neo4j`](https://youtu.be/X-hB2kBFBXs).
**Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA) **Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA)
Deepface also offers facial attribute analysis including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Analysis function under the DeepFace interface is used to find demography of a face. Deepface also offers facial attribute analysis including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Analysis function under the DeepFace interface is used to find demography of a face.
@ -188,6 +166,28 @@ for backend in backends:
[MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/). [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/).
**Tech Stack** - [`Vlog`](https://youtu.be/R8fHsL7u3eE)
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/tech-stack.png" width="90%" height="90%"></p>
The question is that where to store facial representations. You can find vector embeddings of facial images with the represent function.
```python
embedding = DeepFace.represent("img.jpg", model_name = 'Facenet')
```
Recommended tech stack for face verification is mainly based on [relational databases and regular SQL](https://sefiks.com/2021/02/06/deep-face-recognition-with-sql/) or key-value stores such as [Redis](https://sefiks.com/2021/03/02/deep-face-recognition-with-redis/) or [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/). Herein, key-value stores overperform than regular relational databases.
Face verification is a subset of face recognition. In other words, you can run any face verification tool for face recognition as well. However, face verification has O(1) and face recognition has O(n) time complexity. That's why, face recognition becomes problematic with regular face verification tools on millions/billions level data and limited hardware.
You should use some big data solutions in face recognition when the data becomes huge. NoSQL databases comes with the power of map reduce technology. Here, [Hadoop](https://sefiks.com/2021/01/31/deep-face-recognition-with-hadoop-and-spark/) with Spark or Hive will overperform if you have lots of datanodes and clusters. Besides, [mongoDb](https://sefiks.com/2021/01/22/deep-face-recognition-with-mongodb/) is a document database and it is highly scalable as well.
On the other hand, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalability feature. Moreover, [Pinecone](https://sefiks.com/2021/05/19/large-scale-face-recognition-with-pinecone-vector-database/) is fully cloud a-nn / k-nn solution. You should run deepface within those a-nn frameworks if you have really large scale data sets. Those libraries come with high speed but they don't guarantee to find the closest ones always in contrast to k-nn algorithm run in nosql databases.
Finally, graph databases offer to discover relations hard to find. [Neo4j](https://sefiks.com/2021/04/03/deep-face-recognition-with-neo4j/) is a pretty graph database exploring indirect relations between facial images. If you plan to apply unsupervised learning and clustering, then graph databases will work well. It offers k-nn and a-nn algorithms as well.
Here, you can find some implementation demos of deepface with a-nn libraries: [`Elasticsearch`](https://youtu.be/i4GvuOmzKzo) and [`Spotify Annoy`](https://youtu.be/Jpxm914o2xk); key-value stores: [`Redis`](https://youtu.be/eo-fTv4eYzo), [`Cassandra`](https://youtu.be/VQqHs6-4Ylg); and graph databases: [`Neo4j`](https://youtu.be/X-hB2kBFBXs).
## FAQ and Troubleshooting ## FAQ and Troubleshooting
Pre-trained weights of custom models will be downloaded from Google Drive source to your environment once. Download limit of my Google Drive account might be exceeded sometimes. In this case, you might have an exception like "Too many users have viewed or downloaded this file recently. Please try accessing the file again later". You can still download the pre-trained weights from Google Drive manually. You should then download the pre-trained weights to {HOME_FOLDER}/.deepface/weights folder. It won't try to download the weight file if it exists in the weights folder. You can find out your HOME_FOLDER as shown below. Pre-trained weights of custom models will be downloaded from Google Drive source to your environment once. Download limit of my Google Drive account might be exceeded sometimes. In this case, you might have an exception like "Too many users have viewed or downloaded this file recently. Please try accessing the file again later". You can still download the pre-trained weights from Google Drive manually. You should then download the pre-trained weights to {HOME_FOLDER}/.deepface/weights folder. It won't try to download the weight file if it exists in the weights folder. You can find out your HOME_FOLDER as shown below.