From 3af8abd963072b0f78398555ec1d8c2ef0157c15 Mon Sep 17 00:00:00 2001 From: wxywb Date: Wed, 22 Jun 2022 15:57:53 +0800 Subject: [PATCH] revise the doc. Signed-off-by: wxywb --- README.md | 54 +++++++++++++++------------------------------------ cap.png | Bin 0 -> 9015 bytes tabular.png | Bin 0 -> 90489 bytes tabular1.png | Bin 179845 -> 0 bytes tabular2.png | Bin 22954 -> 0 bytes vec1.png | Bin 12487 -> 0 bytes vec2.png | Bin 12250 -> 0 bytes 7 files changed, 16 insertions(+), 38 deletions(-) create mode 100644 cap.png create mode 100644 tabular.png delete mode 100644 tabular1.png delete mode 100644 tabular2.png delete mode 100644 vec1.png delete mode 100644 vec2.png diff --git a/README.md b/README.md index f72af4a..cfcbc6a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Image-Text Retrieval Embdding with BLIP +# Image Captioning with BLIP *author: David Wang* @@ -9,7 +9,7 @@ ## Description -This operator extracts features for image or text with [BLIP](https://arxiv.org/abs/2201.12086) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This is a adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP). +This operator generates the caption with [BLIP](https://arxiv.org/abs/2201.12086) which describes the content of the given image. This is an adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP).
@@ -17,45 +17,33 @@ This operator extracts features for image or text with [BLIP](https://arxiv.org/ ## Code Example -Load an image from path './teddy.jpg' to generate an image embedding. - -Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. +Load an image from path './animals.jpg' to generate the caption. *Write the pipeline in simplified style*: ```python import towhee -towhee.glob('./teddy.jpg') \ +towhee.glob('./animals.jpg') \ .image_decode() \ - .image_text_embedding.blip(model_name='blip_base', modality='image') \ - .show() - -towhee.dc(["A teddybear on a skateboard in Times Square."]) \ - .image_text_embedding.blip(model_name='blip_base', modality='text') \ + .image_captioning.blip(model_name='blip_base') \ + .select() \ .show() ``` -result1 -result2 +result1 *Write a same pipeline with explicit inputs/outputs name specifications:* ```python import towhee -towhee.glob['path']('./teddy.jpg') \ +towhee.glob['path']('./animals.jpg') \ .image_decode['path', 'img']() \ - .image_text_embedding.blip['img', 'vec'](model_name='blip_base', modality='image') \ - .select['img', 'vec']() \ - .show() - -towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \ - .image_text_embedding.blip['text','vec'](model_name='blip_base', modality='text') \ - .select['text', 'vec']() \ + .image_captioning.blip['img', 'text'](model_name='blip_base') \ + .select['img', 'text']() \ .show() ``` -result1 -result2 +result2
@@ -66,7 +54,7 @@ towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \ Create the operator via the following factory method -***blip(model_name, modality)*** +***blip(model_name)*** **Parameters:** @@ -75,33 +63,23 @@ Create the operator via the following factory method ​ The model name of BLIP. Supported model names: - blip_base - -​ ***modality:*** *str* - -​ Which modality(*image* or *text*) is used to generate the embedding. -
## Interface -An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray. +An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption. **Parameters:** ​ ***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* or *str* -​ The data (image or text based on specified modality) to generate embedding. - - - -**Returns:** *numpy.ndarray* - -​ The data embedding extracted by model. - +​ The image to generate embedding. +**Returns:** *str* +​ The caption generated by model. diff --git a/cap.png b/cap.png new file mode 100644 index 0000000000000000000000000000000000000000..0ed342d9dda01ae925a22e8492c1257811768b28 GIT binary patch literal 9015 zcmd5ggF@$t?hcwb4DIG%&Eg>C>bPCcXB_&4s4BZUz#(VEC zzW4rtcfQ$Yt+Vp1*lT}tVze|Aaj?j-0001vvXY!O0DuHS;1U>Uh`S^`_c#E6?dTvY ztEDU}ORMGKYU|)+0{|$+d`L$(OjRZfxnfsVQj)=xlTz7H$J>=x!Qc!F(d|}HLdIko 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