From 24543a09a5ec1a63f73d237da831bcc29af9d060 Mon Sep 17 00:00:00 2001 From: wxywb Date: Fri, 4 Nov 2022 10:50:13 +0800 Subject: [PATCH] replace original images. Signed-off-by: wxywb --- README.md | 10 +++++----- requirements.txt | 5 +++++ tabular1.png | Bin 0 -> 458265 bytes tabular2.png | Bin 0 -> 66083 bytes vec1.png | Bin 0 -> 31817 bytes vec2.png | Bin 0 -> 32530 bytes 6 files changed, 10 insertions(+), 5 deletions(-) create mode 100644 tabular1.png create mode 100644 tabular2.png create mode 100644 vec1.png create mode 100644 vec2.png diff --git a/README.md b/README.md index 3c702f5..6277531 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ ## Description -This operator extracts features for image or text with [ALBEF](https://arxiv.org/abs/2103.00020) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This research introduced a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. This repo is an adaptation from [salesforce / ALBEF](https://github.com/salesforce/ALBEF) +This operator extracts features for image or text with [ALBEF](https://arxiv.org/abs/2107.07651) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This research introduced a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. This repo is an adaptation from [salesforce / ALBEF](https://github.com/salesforce/ALBEF)
@@ -35,8 +35,8 @@ towhee.dc(["A teddybear on a skateboard in Times Square."]) \ .image_text_embedding.albef(model_name='albef_4m', modality='text') \ .show() ``` -result1 -result2 +result1 +result2 *Write a same pipeline with explicit inputs/outputs name specifications:* @@ -54,8 +54,8 @@ towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \ .select['text', 'vec']() \ .show() ``` -result1 -result2 +result1 +result2
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