Marico Computer Vision Project

Marico

Updated 2 years ago

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Classes (45)
Apis Honey
Baggry-s Plain Oats
Bagrry-s SMO
Ching-s Instant Noodles
Coco Soul Virgin Coconut Oil
Dabur Chyawanprash
Dabur Honey
Figaro Extra Virgin Olive Oil
Fortune Soya
Funfoods Peanut Butter Creamy
Funfoods Peanut Butter Crunchy
Funfoods Veg Mayonnaise Jar 250gm
Funfoods Veg Mayonnaise Pouch
Gaia Coconut Oil
Gaia Extra Virgin Olive Oil
Immuniveda Chyawanprash
Kellog-s Plain Oats
Kissan Peanut Butter Creamy
Kissan Peanut Butter Crunchy
Maggi Instant Noodles
Patanjali Chyawanprash
Patanjali Honey
Quaker Plain Oats
Quaker SMO
Ruchi Soya
Saffola Aura -olive Oil-
Saffola Carton packed Honey Jar
Saffola Honey
Saffola Masala Oats
Saffola Mayo Veg 250Gm Jar
Saffola Mayonnaise Jar
Saffola Mayonnaise Pouch
Saffola Peanut Butter
Saffola Plain Oats
Saffola Soya Chunks
Saffola_Oodles
Saffola_Salt
Tata Salt
Veeba Eggless Mayonnaise Jar
Veeba Eggless Mayonnaise Pouch
Veeba Peanut Butter Creamy
Veeba Peanut Butter Crunchy
Yipee Instant Noodles
Zandu Chawanprash
Zandu Honey

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Description

Here are a few use cases for this project:

  1. Intelligent Retail Inventory Management: The model can be used to automate tracking of inventory in real-time on retail shelves. It can identify low stock and trigger re-stocking procedures ensuring adequate supply of products.

  2. Retail Checkout Automation: The model can be integrated into self-checkout systems. It can identify items in shopping carts/baskets, preventing the need for manual scanning, and thus speeding up the checkout process.

  3. Market Analysis and Research: The visibility and popularity of brands on commercial shelves can be analyzed using this model, informing data-driven decisions for businesses in the retail sector.

  4. Price Comparison Applications: Applications that aid shoppers by providing price comparison across stores can use this model to gather data by identifying products from images captured in different stores.

  5. Consumer Trend Analysis: By consistently capturing and analyzing images from retail stores, the model can help track consumer buying trends by identifying the frequency and quantity of particular products being sold.

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Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            marico-tg0wc_dataset,
                            title = { Marico Dataset },
                            type = { Open Source Dataset },
                            author = { Marico },
                            howpublished = { \url{ https://universe.roboflow.com/marico/marico-tg0wc } },
                            url = { https://universe.roboflow.com/marico/marico-tg0wc },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-11-21 },
                            }
                        
                    

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