Logo Recognition Model: a mix of UAE companies and global enterprises

Model Details

Model Name: Falconsai/brand_identification
Base Model: google/vit-base-patch16-224-in21k
Model Type: Vision Transformer (ViT) - Image Classification
Version: 1.0
License: MIT
Author: Michael Stattelman from Falcons.ai

Overview

This model is a fine-tuned version of Google's Vision Transformer (ViT) vit-base-patch16-224-in21k, specifically trained for the task of classifying UAE company logos. It was trained on a custom dataset consisting of logos from various brands and companies based in the United Arab Emirates as well as others.

Primary Use Cases:

The primary use case for this model is to classify images of logos into their respective UAE-based companies. This can be particularly useful for applications in brand monitoring, competitive analysis, and marketing research within the UAE market.

Marketing and Advertising Analytics:
- Analyzing the presence and frequency of brand logos in various media channels (TV, social media, websites) to measure brand visibility and effectiveness of advertising campaigns.
Brand Monitoring and Protection:
- Monitoring where and how often a brand's logo appears online (social media, blogs, forums) to protect against misuse or unauthorized brand representation.
Market Research:
- Studying consumer behavior and preferences by analyzing the prevalence of different brand logos in public spaces or events.
Competitive Analysis:
- Comparing the visibility of different brands within a specific market or industry segment based on logo recognition data.
Retail and Inventory Management:
- Automating inventory tracking by recognizing product brands through their logos, which helps in maintaining stock levels and identifying popular products.
Augmented Reality and Virtual Try-On:
- Enhancing augmented reality experiences by recognizing brand logos on products or packaging to overlay additional information or virtual elements.
Customer Engagement and Personalization:
- Enhancing customer experiences by recognizing brands that customers interact with, which can personalize marketing messages or recommendations.
Event Management and Sponsorship Tracking:
- Tracking sponsor logos at events and venues to evaluate sponsorship effectiveness and compliance with branding agreements.
Security and Authentication:
- Verifying the authenticity of products or documents by recognizing the presence and correct placement of brand logos.
Content Filtering and Moderation:
- Filtering or moderating content on social media platforms based on the presence of recognized brand logos to ensure compliance with brand guidelines or prevent misuse.

These are just a few examples of how a Falconsai/brand_identification logo recognition model can be applied across different industries and purposes. The ability to accurately identify brand logos can provide valuable insights and efficiencies in various business operations.

Direct Use

Upload an image of a logo to the model to get a classification label.
Integrate the model into applications or services that require logo recognition.

Downstream Use

Incorporate the model into larger systems for automated brand analysis.
Use the model as part of a tool for sorting and categorizing images by brand.

Model Description

Architecture

The base model used is the Vision Transformer vit-base-patch16-224-in21k, which uses self-attention mechanisms to process image patches. The fine-tuning process adapted this pre-trained model to recognize and classify specific logos from UAE companies.

Training Data

The model was trained on a curated dataset of UAE company logos as well as others of international companies. The dataset consists of thousands of images across various brands to ensure robustness and accuracy.

Performance

The model achieved high accuracy on a held-out validation set, indicating strong performance in classifying UAE company logos. Detailed performance metrics (accuracy, precision, recall, F1-score) can be provided upon request.

How to Use

To use the model for inference, you can load it using the transformers library from Hugging Face:

import torch
from PIL import Image
from transformers import AutoModelForImageClassification, ViTImageProcessor

image = Image.open('<path_to_image>')
image = image.convert("RGB")  # Ensure image is in RGB format

# Load model and processor
model = AutoModelForImageClassification.from_pretrained("Falconsai/brand_identification")
processor = ViTImageProcessor.from_pretrained("Falconsai/brand_identification")

# Preprocess image and make predictions
with torch.no_grad():
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])

Companies Identified:

Abu Dhabi Islamic Bank
Acer
Adidas
Adnoc
Aldar
Alienware
Amazon
AMD
Apple
Asus
Beats by Dre
Blackberry
Bose
Careem
Cisco Systems
Coke
D-Link
Dell
Delonghi
DP World
Du
E&
Emaar
Emirates
Emirates NBD
Etisalat
Falcons.ai
First Abu Dhabi Bank
Fujitsu
Google
GoPro
HEC
Hewlett Packard
Hilti
Hisense
Huawei
IBM
Khaleej Times
L'Oréal
Lenovo
LG
LinkedIn
Louis Vuitton
Majid Al Futtaim
Mashreq
Maybelline
McDonalds
Mercedes
Meta
Microsoft
MSI
Nike
Nvidia
OpenAI
Puma
Rakez
Samsung
Snapdragon
Tesla
Ubuntu
Virgin
Zwag

Limitations and Biases

The model is specifically trained on UAE company logos and may not perform well on logos from companies outside the UAE.
The model's performance is contingent upon the quality and diversity of the training dataset.
Potential biases in the training data can lead to biases in model predictions.

Ethical Considerations

Ensure that the use of this model complies with local regulations and ethical guidelines, especially concerning privacy and data security.
Be mindful of the limitations and biases and do not use the model in critical applications without thorough validation.

Acknowledgements

This model was developed and fine-tuned by Michael Stattelman from Falcons.ai, leveraging the base Vision Transformer model provided by Google.

Contact Information

For further information, questions, or collaboration requests, please contact:

Name: Michael Stattelman
Affiliation: Falcons.ai
URL: https://falcons.ai