Akshit Mehra - Labellerr

Announcement: We're launching LabelGPT, World's fastest prompt based labeling tool. Join the waiting list to get beta access

Akshit Mehra

Akshit is a Computer Science graduate from IIT Ropar. He writes on topic of computer vision, deep learning, LLMs finetuning and Tensorflow. He is working with Labellerr as an Associate ML Engineer.

Recognize Anything: A Strong Image Tagging Model

computer vision

Recognize Anything: A Large Model For Multi Label Tagging

In the realm of multi-label image recognition, often referred to as image tagging, the objective is to assign semantic labels to images by identifying multiple relevant labels for a given image. This task holds considerable significance in computer vision, given that images inherently encompass multiple labels pertaining to objects, scenes,

Tutorial To Leverage Open AI's CLIP Model For Fashion Industry

Tutorial To Leverage Open AI's CLIP Model For Fashion Industry

In recent years, the field of artificial intelligence and machine learning has made significant strides, enabling researchers and developers to achieve groundbreaking outcomes. Among these advancements, the CLIP (Contrastive Language–Image Pretraining) model by OpenAI has emerged as a revolutionary breakthrough in AI. Leveraging its multimodal capacity, CLIP is remarkably

Transforming Computer Vision: The Rise of Vision Transformers and Their Impact

computer vision

Transforming Computer Vision: The Rise of Vision Transformers And Its Impact

As introduced by Vaswani et al. in 2017, self-attention-based architectures, particularly Transformers, have become the favored choice in natural language processing (NLP). The prevailing approach involves pre-training these models on extensive text corpora and fine-tuning them on more specific tasks, commonly referred to as fine-tuning. Transformers have demonstrated remarkable computational

After studying the Meta Co-Tracker, This is What We Have to Say About it.

computer vision

Meta Launched Co-Tracker! This Is What We Have to Say About It.

Last Month, Meta AI released a deep learning-based model for tracking objects in a video motion called Co-Tracker. Co-Tracker is a fast transformer-based model that can track any point in a video. It brings to tracking some of the benefits of Optical Flow. Co-Tracker can track: 1. Every pixel in

What different methods to employ for Data Labelling in Machine Learning Tasks

Machine Learning

Different Methods To Employ Data Labelling In Machine Learning Tasks

Often, data labeling goes hand in hand with data acquisition. When compiling information from the Web to create a knowledge base, each fact is considered accurate and is therefore implicitly labeled as true. In the context of data labeling literature, it's useful to separate it from data acquisition due to

Hands-on with Fine-Tuning LLM

fine-tuning of large language models

Fine-Tuning Tutorial: Falcon-7b LLM To A General Purpose Chatbot

LLM models undergo training on extensive text data sets, equipping them to grasp human language in depth and context. In the past, most models underwent training using the supervised method, where input features and corresponding labels were fed. In contrast, LLMs take a different route by undergoing unsupervised learning. In

Technical Knowhow for Fine-Tuning Model using Reinforcement Learning with Human Feedback

reinforcement learning with human feedback

Complete Guide On Fine-Tuning LLMs using RLHF

In discussions about why ChatGPT has captured our fascination, two common themes emerge: 1. Scale: Increasing data and computational resources. 2. User Experience (UX): Transitioning from prompt-based interactions to more natural chat interfaces. However, there's an aspect often overlooked – the remarkable technical innovation behind the success of models like ChatGPT.

Comprehensive Guide for Fine Tuning of LLMs

Large Language Models

Everything You Need To Know About Fine Tuning of LLMs

Pre-trained large language models (LLMs) come with impressive capabilities, such as text generation, summarization, and coding. Nevertheless, these LLMs are only universally suitable solutions for some scenarios. Depending on your specific application, you may encounter tasks the language model cannot handle successfully. In such situations, one viable approach is to

Top 6 LLMs Applications No One Is Talking About

Top 6 LLMs Applications No One Is Talking About

A Large language model (LLM) is a form of machine learning model capable of performing various natural language processing (NLP) tasks, including text generation, text classification, conversational question answering, and language translation. The term "large" pertains to the substantial number of values (parameters) that the language model can autonomously adjust

Challenges faced during the Development and usage of LLMs

Large Language Models

Top 8 Challenges Of Building Own Large Language Model

Large Language Models (LLMs) are advanced artificial intelligence systems that have been trained on massive amounts of text data to understand and generate human-like language. These models have the capability to generate coherent and contextually relevant text, making them valuable tools for various natural language processing tasks, including text generation,

Challenges in LLM Adoption for Enterprises: Introducing LangKit for Secure LLM Adoption

Security Challenges in LLM Adoption for Enterprises And How To Solve Them

In 2023, generative AI gained significant attention, but enterprises are facing challenges in identifying suitable use cases for this technology. Large Language Models (LLMs) have inherent limitations, such as generating irrelevant or off-topic content and being vulnerable to prompt injection, making them a source of concern for businesses. Figure: LLMs

Securing AI Systems: Insights from Google's AI Red Team

Securing AI System's Importance: Learning from Google's AI Red Team

Red teaming is a critical cybersecurity practice that involves creating a team of ethical hackers, known as the "Red Team," to simulate real-world attacks and adversarial scenarios on an organization's systems, networks, or technology. In the context of AI systems, red teaming plays a decisive role in preparing organizations for

Grounded DINO: Combining DINO with Grounded Pre-training for Open-set Object Annotations

Enhanced Zero-shot Labeling through the Fusion of DINO and Grounded Pre-training

Visual intelligence requires the ability to understand and detect novel concepts. In the recent CVPR 2023, which occurred in June, a paper was published introducing Grounding DINO. The authors aim to develop a robust system capable of detecting arbitrary objects specified through human language inputs. They refer to this task

Comparison between Labellerr and different open source tools

Common Challenges and Solutions for Free Data Labeling Tools

Data labeling is of utmost importance in the field of machine learning and artificial intelligence as it provides labeled data, acting as ground truth, essential for training supervised learning models. The process involves annotating data with relevant labels, enabling algorithms to learn patterns and make accurate predictions. Data labeling is

CVPR 2023: Advancements, Trends, and Future Directions in Computer Vision

CVPR 2023: Key Takeaways & Future Trends

The atmosphere was charged with excitement at CVPR 2023 in Vancouver, the renowned conference for computer vision. Researchers and practitioners gathered to exchange ideas about the significant advancements in the field over the past year and discuss the future of computer vision and AI as a whole. The predominant subjects

LabelGPT: Revolutionizing Computer Vision with AI-Powered Data Annotation

Elevating ML: AI-Driven Data Annotation with LabelGPT

Data labeling is a crucial step in the process of training machine learning and artificial intelligence models. It involves annotating or tagging data samples to provide meaningful and accurate information that can be used to train algorithms. Data labeling helps machines understand and interpret the data they receive, enabling them

Detecting, Monitoring, and Correcting Monitoring Drift in Computer Vision Systems

computer vision

Computer Vision Data Drift - Why It's Important To Manage & How?

As the circumstances in which a computer vision model operates change, the model needs to be updated to ensure its adaptability. For instance, consider a model used to identify objects in a retail store. As new products are introduced, store layouts are modified, and lighting conditions fluctuate, the model's performance

Edge AI: Deployment of AI/ML Models over Edge Devices Via Compression of Machine Learning Models

Edge AI: Deployment of AI/ML Models Via Compression of Machine Learning Models

Are you confident that maximum potential is being achieved in neural networks? It's possible that untapped opportunities are being overlooked! Most AI developers utilize various optimization techniques for their neural networks. Some developers rely on built-in features of frameworks like PyTorch, while others invest significant time integrating with tools like

Human Activity Recognition (HAR): Fundamentals, Models, Datasets

human activity recogonition

Human Activity Recognition (HAR): Fundamentals, Models, Datasets

Human activity recognition (HAR) refers to using computer and machine vision technology to interpret and understand human motion. HAR involves analyzing sensor-recorded data to interpret various forms of human motion, including activities, gestures, and behaviors. This data is then translated into actionable commands that computers can execute and analyze using

Insights And Best Practices For Building and Deploying Computer Vision Models

Insights And Best Practices For Building and Deploying Computer Vision Models

Building and deploying computer vision (CV) models require a deep understanding of both the theoretical aspects of computer vision and the practical challenges of developing robust and efficient systems. Computer Vision Engineers play a critical role in this process, leveraging their expertise to design, train, and deploy CV models that

computer vision

Foundation Models for Image Search: Enhancing Efficiency and Accuracy

Traditional image search engines employ intricate algorithms to match user-provided keywords with images stored in their database. These algorithms heavily rely on explicit features such as image descriptions, colors, patterns, shapes, and accompanying textual information from webpages. Developing these algorithms demands significant engineering effort and time, and they need help

A hands-on for creating and End to End machine learning pipeline in Python.

Machine Learning

How to Build an End-to-End ML Pipeline

Machine learning pipeline refers to the complete workflow and processes of building and deploying a machine learning model. It automates and standardizes the workflow involved in creating a machine-learning model. A machine learning pipeline consists of sequential steps, which include data extraction and preprocessing to model training and deployment. It

Advancing Performance in Automating Invoice Processing

Machine Learning

Advancing Performance in Automating Invoice Processing

Large companies dealing with a high volume of invoices daily show great interest in automatic invoice processing systems. These systems are attractive not only because of their legal requirement to store invoices for years but also due to economic reasons. Figure: Bills and Invoices Cristani et al. compared manually processed

Exploring the COCO Dataset and its Applications

computer vision

Exploring the COCO Dataset and its Applications

The COCO (Common Objects in Context) dataset is a widely used benchmark dataset in computer vision. It was created to facilitate the developing and evaluation of object detection, segmentation, and captioning algorithms. The COCO dataset consists of an extensive collection of images that depict everyday scenes with various objects in

Training Small-Scale Vs Large-Scale Language Models: The Difference

Large Language Models

Training Small-Scale Vs Large-Scale Language Models: The Difference

Language model development has significantly evolved, leading to the emergence of small-scale and large-scale models. These models have revolutionized various natural language processing related tasks and profoundly impacted the artificial intelligence field. Understanding the differences between small-scale and large-scale language models is crucial for grasping their capabilities and implications. Figure: