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

running times removed

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2020-06-19 14:00:57 +03:00 committed by GitHub
parent 7df474934c
commit 70e9af954e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 0 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
README.md
View File

@ -65,13 +65,6 @@ for model in models:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model)
```
The complexity and response time of each face recognition model is different so do accuracy scores. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table.
| Model | VGG-Face | OpenFace | Google FaceNet | Facebook DeepFace |
| --- | --- | --- | --- | --- |
| Building | 2.35 s ± 46.9 ms | 6.37 s ± 1.28 s | 25.7 s ± 7.93 s | 23.9 s ± 2.52 s |
| Verification | 897 ms ± 38.3 ms | 616 ms ± 12.1 ms | 684 ms ± 7.69 ms | 605 ms ± 13.2 ms |
**Passing pre-built face recognition models**
You can build a face recognition model once and pass this to verify function as well. This might be logical if you need to call verify function several times.
@ -121,13 +114,6 @@ print("Race: ", demography["dominant_race"])
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-2.jpg" width="95%" height="95%"></p>
Model building and prediction times are different for those facial analysis models. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table.
| Model | Emotion | Age | Gender | Race |
| --- | --- | --- | --- | --- |
| Building | 243 ms ± 15.2 ms | 2.25 s ± 34.9 | 2.25 s ± 90.9 ms | 2.23 s ± 68.6 ms |
| Prediction | 389 ms ± 11.4 ms | 524 ms ± 16.1 ms | 516 ms ± 10.8 ms | 493 ms ± 20.3 ms |
**Passing pre-built facial analysis models**
You can build facial attribute analysis models once and pass these to analyze function as well. This might be logical if you need to call analyze function several times.