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Sefik Ilkin Serengil 2020-06-19 14:00:57 +03:00 committed by GitHub
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@ -65,13 +65,6 @@ for model in models:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model) 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** **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. 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> <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** **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. 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.