From 985828d0a386e6179ac2d2f415a3fbfad5b53f29 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Sun, 5 May 2024 07:52:03 +0100 Subject: [PATCH] more lintings on readme --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 8de48ab..48686d2 100644 --- a/README.md +++ b/README.md @@ -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.