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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. 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 ```python
embedding = embedding_objs[0]["embedding"] for embedding_obj in embedding_objs:
assert isinstance(embedding, list) embedding = embedding_obj["embedding"]
assert ( assert isinstance(embedding, list)
assert (
model_name == "VGG-Face" model_name == "VGG-Face"
and len(embedding) == 4096 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. 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.