From d8138b1a44ea402b646b3e1e317ed4a2d79f6c20 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Sat, 16 Jul 2022 11:50:48 +0100 Subject: [PATCH] embeddings --- README.md | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/README.md b/README.md index 4b69c70..cbb308a 100644 --- a/README.md +++ b/README.md @@ -72,14 +72,7 @@ Face recognition models basically represent facial images as multi-dimensional v embedding = DeepFace.represent(img_path = "img.jpg") ``` -This function returns an array as output. The size of the output array would be different based on the model name. For instance, VGG-Face is the default model for deepface and it represents facial images as 2622 dimensional vectors. - -```python -assert isinstance(embedding, list) -assert len(embedding) == 2622 -``` - -Here, embedding is also plotted with 2622 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. +This function returns an array as output. The size of the output array would be different based on the model name. For instance, VGG-Face is the default model for deepface and it represents facial images as 2622 dimensional vectors. Here, embedding is also plotted with 2622 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.