From de1b0a84c171078f01eaf54a21fea0faaadebfdc Mon Sep 17 00:00:00 2001 From: ldy Date: Wed, 18 Jun 2025 08:12:02 +0000 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20readme.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/readme.md b/readme.md index bfe9568..9aa5cbe 100644 --- a/readme.md +++ b/readme.md @@ -7,8 +7,8 @@ We derived the image embeddings by using a CLIP encoder and mapping it with the ### Prerequisites 1. install requirements.txt -2. Make sure you have downloaded [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin") -3. For example image data, I use [2017 Val images 5K/1GB](http://images.cocodataset.org/zips/val2017.zip) and [2017 Train/Val annotations 241MB](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) +2. Make sure you have [llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5/tree/main) under your **models** folder. +3. For example image data, I used [2017 Val images 5K/1GB](http://images.cocodataset.org/zips/val2017.zip) and [2017 Train/Val annotations 241MB](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) ### Usage