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Sefik Ilkin Serengil 2024-05-05 07:52:03 +01:00 committed by GitHub
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@ -98,12 +98,13 @@ embedding_objs = DeepFace.represent(
This function returns an array as embedding. The size of the embedding array would be different based on the model name. For instance, VGG-Face is the default model and it represents facial images as 4096 dimensional vectors.
```python
embedding = embedding_objs[0]["embedding"]
assert isinstance(embedding, list)
assert (
model_name == "VGG-Face"
and len(embedding) == 4096
)
for embedding_obj in embedding_objs:
embedding = embedding_obj["embedding"]
assert isinstance(embedding, list)
assert (
model_name == "VGG-Face"
and len(embedding) == 4096
)
```
Here, embedding is also [plotted](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) with 4096 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right. Similar to 2D barcodes, vertical dimension stores no information in the illustration.