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README

This project implements a picture and recipe retrieval system based on milvus. That is, input a food picture and return the recipe corresponding to the picture.

This project is based on the project im2recipe-Pytorch.

Data Introduction

Data introduction

The data for this project comes from recipe1M. Recipe1M+ is a new large-scale, structured corpus of over one million cooking recipes and 13 million food images.

Loading data

The dataset in recipe1M is used in this project. You can download the dataset from the recipe1M website. This project also provides some data sets and models.

Link: https://pan.baidu.com/s/1GpPqRTjiBen0qoudAWZn6g Extraction code: bptf

Script Description

This project contains two parts, service and webclient.

service provides the code of the back-end service. webclient provides scripts for the front-end interface.

The configuration file config.py in service explains:

Parameter	Description	Default
MILVUS_HOST	Milvus service ip	127.0.0.1
MILVUS_PORT	Milvus service port	19530
MYSQL_HOST	MySql service ip	127.0.0.1
MYSQL_PORT	MySql service port	3306
MYSQL_USER	MySql user name	root
MYSQL_PASSWORD	MySql password	123456
MYSQL_DATABASE	MySql database name	mysql
TABLE_NAME	Default table name	recipe
data_path	The path of the dataset lmdb	data/test_lmdb
file_keys_pkl	The path of the file test_keys.pkl	data/test_keys.pkl
recipe_json_fname	The path of the file recipe1M/layer1.json	data/recipe1M/layer1.json
im_path	When querying, the client upload image storage path	data/ima_test
model_path	tThe path of the model	data/model/model_e500_v-8.950.pth.tar
ingrW2V	Parameters of the Milvus collection	data/vocab.bin
temp_file_path	The path of the Temporary file	temp.csv
collection_param	Parameters of the Milvus collection	default
search_param	Parameters of Milvus search	16
top_k	The number of recipes displayed as a result	5

Steps to build a project

1. Install Milvus0.10.4.
2. Install MySql.
3. Clone project

git clone https://github.com/milvus-io/bootcamp.git
cd bootcanp/solution/im2recipe

4. Installation dependencies

pip3 install -r requirement.txt

5. import data

python load.py

Before importing, check whether the path of the parameter data_path in config.py is correct. Note: This process takes a long time

6. start service

uvicorn main:app

7. start client

docker run -d -p 80:80 -e API_URL=http://127.0.0.1:8000 zilliz/milvus-search-food-recipes:latest

API_URL specifies the IP and port of the server