Omkar2118/datasets huggingface dataset:
Weld Seam Inspection

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Weld-Seam-Inspection/" target="_blank">👉 Download the case studies here</a>

An automotive manufacturer was experiencing high levels of defects in weld seams during the production process. These defects were affecting the quality of the final products, leading to increased rework, higher production costs, and delays. The manufacturer required a reliable solution to detect and inspect weld seams automatically, ensuring consistent product quality while improving production efficiency.

Challenge

Manual analysis of medical images is labor-intensive and susceptible to inconsistencies.

Diagnostic delays often occur due to the lack of skilled radiologists and the increasing volume of medical imaging data.

Detecting subtle anomalies in images like X-rays, MRIs, and CT scans requires advanced expertise, making it challenging to maintain consistent accuracy.

There was a need for an automated, real-time diagnostic tool to support healthcare professionals in decision-making.

Solution Provided

An AI-based weld defect detection system was developed to capture, analyze, and identify defects in real-time on the production line. Utilizing a convolutional neural network deployed on an NVIDIA Jetson Nano, the system efficiently classifies defects, integrates with monitoring systems for automatic flagging, and continuously improves through ongoing data feedback.

Development Steps

Data Collection

Installed high-definition cameras to capture and label weld seam images.

Preprocessing

Normalized images and enhanced key features to prepare for training.

Model Training

Developed a CNN using transfer learning and augmented the dataset for robustness.

Validation

Tested the model on unseen data to ensure accuracy and reliability before deployment.

Deployment

Implemented the trained model on an NVIDIA Jetson Nano for real-time defect detection.

Integration & Improvement

Connected the system to production monitoring and established a feedback loop for continuous model enhancement.

Results

Increased Conversion Rates

Personalized product recommendations led to a 15 increase in conversion rates, as guests were more likely to find products that matched their preferences.

Advanced customer Retention

By delivering applicable and engaging exploits, the platform erected stronger connections with guests, enhancing dedication and duplication purchases.

Advanced Average Order Value

Adapted suggestions encouraged guests to add complementary particulars to their carts, performing in a conspicuous increase in average order value.

Enhanced customer Satisfaction

Substantiated marketing created a indefectible and enjoyable shopping experience, leading to positive customer feedback and bettered brand character.

Real- time severity

The system’s capability to adapt to real- time relations assured those recommendations remained applicable, indeed as customer preferences changed.
LeetCode-Hard

LeetCode-Hard is a benchmark dataset for code generation, consisting of 40 challenging LeetCode "hard-level" questions across 19 programming languages. It is designed to evaluate the problem-solving and functional correctness capabilities of large language models (LLMs), particularly in handling complex algorithmic tasks. This dataset was used to assess the Reflexion framework, which leverages verbal reinforcement learning to improve LLM performance on difficult coding problems.

Introducing paper: Reflexion: Language Agents with Verbal Reinforcement Learning
Karrierewege

Karrierewege Dataset

Introducing paper: KARRIEREWEGE: A Large Scale Career Path Prediction Dataset
Karrierewege+

Karrierewege+ Dataset

Introducing paper: KARRIEREWEGE: A Large Scale Career Path Prediction Dataset
VPData

The largest video inpainting dataset comprises over 390K clips (> 866.7 hours), featuring precise masks and detailed video captions.

Introducing paper: VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
VPBench

The benchmark for VPData, the largest video inpainting dataset, which comprises over 390K clips (> 866.7 hours) and features precise masks and detailed video captions.

Introducing paper: VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control