github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-04 02:20:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add Angular Distance to Documentation

Browse Source
This commit is contained in:
haddyadnan 2025-04-07 22:51:57 +03:00
parent 9e334057df
commit b69e7ceb21
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -150,10 +150,10 @@ Conducting experiments with those models within DeepFace may reveal disparities
Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent faces as vectors. We expect that a face pair of same person should be [more similar](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/) than a face pair of different persons.
Similarity could be calculated by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance or L2 normalized Euclidean. The default configuration uses cosine similarity. According to [experiments](https://github.com/serengil/deepface/tree/master/benchmarks), no distance metric is overperforming than other.
Similarity could be calculated by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Angular Distance, Euclidean Distance or L2 normalized Euclidean. The default configuration uses cosine similarity. According to [experiments](https://github.com/serengil/deepface/tree/master/benchmarks), no distance metric is overperforming than other.
```python
metrics = ["cosine", "euclidean", "euclidean_l2"]
metrics = ["cosine", "euclidean", "euclidean_l2", 'angular']
result = DeepFace.verify(
img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1]