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some more linting on readme code

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Sefik Ilkin Serengil 2024-05-05 07:50:30 +01:00 committed by GitHub
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@ -100,7 +100,10 @@ This function returns an array as embedding. The size of the embedding array wou
```python ```python
embedding = embedding_objs[0]["embedding"] embedding = embedding_objs[0]["embedding"]
assert isinstance(embedding, list) assert isinstance(embedding, list)
assert model_name == "VGG-Face" and len(embedding) == 4096 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. 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.
@ -140,8 +143,9 @@ dfs = DeepFace.find(
) )
#embeddings #embeddings
embedding_objs = DeepFace.represent(img_path = "img.jpg", embedding_objs = DeepFace.represent(
model_name = models[2] img_path = "img.jpg",
model_name = models[2]
) )
``` ```