Landing.ai

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Introduction:
Landing AI offers LandingLens, a cloud-based computer vision platform for creating custom projects easily.
Added on:
Mar 07 2023
Monthly Visitors:
195.4K
Social & Email:
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Landing.ai Product Information

What is Landing.ai?

Landing AI is a computer vision platform and AI software company that offers a cloud-based computer vision software platform called LandingLens. It allows users to create custom computer vision projects in minutes via natural prompting interactions, making it fast, easy, and intuitive to build computer vision systems.

How to use Landing.ai?

To use LandingLens, users can upload images by dragging and dropping them directly into the platform or capturing live images from their webcam. They can label objects in the images using the LandingLens labeling tool, which helps create high-quality training data for maximum accuracy. After labeling, users can train their model and evaluate its performance. Once the model is accurate enough, it can be deployed to the cloud or edge devices with just a few mouse clicks. Users can monitor the model's performance and update it as needed.

Landing.ai's Core Features

Visual Prompting: Fast and easy way to have vision-based interactions by prompting the system

Data-Centric AI: Ensures models work even with small datasets by ensuring data quality

Flexible Deployment: Cloud and edge device deployment options to integrate into existing environments

Unlimited Scalability: Allows scaling projects from a single production line to worldwide operations

Landing.ai's Use Cases

#1

Automotive: Quality inspection, defect detection, and autonomous driving

#2

Electronics: Quality control, PCB inspection, and component recognition

#3

Food & Beverage: Quality assurance, contamination detection, and package inspection

#4

Medical Devices: Defect identification, product verification, and quality control

#5

Life Sciences: Cell identification, drug discovery, and research analysis

#6

Agriculture: Crop monitoring, disease detection, and yield optimization

#7

Manufacturing: Production line monitoring, defect detection, and process optimization

#8

Infrastructure: Facility management, safety monitoring, and object recognition

#9

Pharma: Drug packaging verification, counterfeit detection, and quality control

#10

EV Manufacturing: Battery inspection, component recognition, and production optimization

FAQ from Landing.ai

What is Landing AI?

How does LandingLens work?

What are the core features of LandingLens?

What industries can benefit from LandingLens?

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Analytic of Landing.ai

Landing.ai Website Traffic Analysis

Visit Over Time

Monthly Visits
195.4K
Avg.Visit Duration
00:02:24
Page per Visit
4.91
Bounce Rate
39.28%
Dec 2022 - Feb 2025 All Traffic

Geography

Top 5 Regions

United States
19.15%
China
8.32%
India
7.23%
Singapore
6.10%
Vietnam
5.59%
Dec 2022 - Feb 2025 Desktop Only

Traffic Sources

Search
44.87%
Direct
40.46%
Referrals
9.46%
Social
4.54%
Display Ads
0.57%
Mail
0.10%
Dec 2022 - Feb 2025 Worldwide Desktop Only

Top Keywords

Keyword
Traffic
Cost Per Click
landing ai
9.3K
$ 2.05
agentic object detection
--
vision agent
--
landingai
--
$ 1.92
landinglens
--
$ 4.23

Social Listening

All
YouTube
Tiktok
38:26

Visual Prompting Livestream With Andrew Ng

Try out Visual Prompting today at https://landing.ai And join the conversation in the Landing AI community at community.landing.ai/c/visual-prompting/ [About Visual Prompting Livestream] The traditional AI modeling workflow requires multiple steps: (i) finding and labeling data, (ii) training a model, and then (iii) making predictions. In contrast, text interfaces like ChatGPT have a dramatically simpler process where a user can give a text prompt saying what they want, and get an answer quickly. This has revolutionized NLP (natural language processing). Traditional AI: Label→ Train → Predict (taking days/months) Prompting based AI: Prompt → Predict (taking minutes/seconds) In this livestream presentation, Andrew Ng will share some early thoughts – and present Landing AI’s results – on generalizing this concept from text to computer vision, so that users can input a simple Visual Prompt (that indicates a few things on an image) and quickly get a result. Meta’s SAM model is one example of Visual Prompting, applied to image segmentation of individual images. In the next few years, Andrew expects that Visual Prompting tools will make computer vision much more accessible, just as text prompting has for NLP. Join Andrew to discuss: * Lessons from NLP (transformers and large language models) for computer vision (vision transformers, foundation vision models) * Visual Prompting as a fast, easy and natural way to have a vision-based interaction * Live demo * Implications of prompting on machine learning project lifecycle * Live Q&A

LandingAI
Apr 25 2023
87.2K
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