Yashika Yadav’s Post

🚨 Team Tickets Expire This Thursday, 9/5! 🚨 Time’s running out to secure your team’s passes for The AI Conference 2024! Get exclusive access to cutting-edge sessions, top-tier networking, and industry-leading insights! Link in Bio! #theaiconference #techindustry #engineers #innovators #programmers #software #robotics #TAIC2024 #data #developers

Annotating this image would have taken me 15-30 minutes manually. With Magic Label, it took just 5 seconds. Now imagine that kind of time-saving across your entire dataset. Roboflow #ComputerVision #DataAnnotation

**LinkedIn Day 11:** Today's challenge is to check if a robot returns to the origin after a series of moves. The solution counts vertical and horizontal moves separately: 'U' increases vertical count, 'D' decreases it, 'L' increases horizontal count, and 'R' decreases it. If both counts are zero at the end, the robot is back at the origin. For example, moves like "UD" return the robot to the origin, while "LL" do not. This approach efficiently verifies the robot's final position based on the given moves. #100DaysOfCode #CodingChallenge 🚀🤖

🚀 Day 3: LeetCode Daily Challenge Completed! 🚀 Today's problem was Problem #874: Walking Robot Simulation. My journey involved two distinct approaches: Initial Method: Complex Handling: The initial code had a complex logic for obstacle detection and movement that resulted in inefficient execution and time limit exceed errors. Obstacle Check: It used multiple conditions to adjust the robot's position, which was error-prone and difficult to optimize. Direction Handling: Direction changes were managed with cumbersome logic that led to confusing state transitions. Final Method: Optimized Approach: Streamlined the logic to handle movements and turns more efficiently, reducing unnecessary checks and operations. Efficient Obstacle Handling: Implemented a more direct approach to detect and handle obstacles, ensuring that the robot's path is updated accurately. Simplified Direction Changes: Improved the handling of direction changes with clearer logic for turning right or left. Thrilled to see progress and eager to tackle more challenges! 💪 #LeetCode #DailyChallenge #Coding #ProblemSolving #LinkedInSeries

#OpenVLA, a 7B-parameter open-source vision-language-action model (VLA), trained on 970k robot episodes from the Open X-Embodiment dataset ✅ OpenVLA sets a new state of the art for generalist robot manipulation policies. It supports controlling multiple robots out of the box and can be quickly adapted to new robot domains via parameter-efficient fine-tuning. ✅ The OpenVLA checkpoints and #PyTorch training pipeline are fully open-source and models can be downloaded and fine-tuned from #HuggingFace 👉 Paper https://lnkd.in/dK6x8jw5 👉 Project https://openvla.github.io/ ♻️𝒌𝒊𝒏𝒅𝒍𝒚 𝒓𝒆𝒑𝒐𝒔𝒕♻️

🌟 Day 57: Continuing the Journey! 🌟 Today, I solved: Walking Robot Simulation (Problem 874) Each problem brings new learning and strengthens my problem-solving abilities. The journey of growth continues! #100DaysOfCode #DSA #LeetCode #ProblemSolving #TechSkills #CodingJourney #AlgorithmChallenge Here's the solution to the problem:

🚀 We Trained a YOLOv8 Model to Detect Bolts in Real-Time — Here's How! 🛠️ Recently, my friends and I completed an exciting project: we trained a YOLOv8 model to recognize bolts in real-time using just a webcam, ROS Humble, and Ubuntu 20.04. 🧠 But it wasn't just about bolts—it was about pushing the limits of machine learning and computer vision in practical applications like automation and quality control. 💡 What We Did: Integrated YOLOv8 with ROS for live video detection Tuned our model for high accuracy and real-time performance Used a simple webcam as our input to detect bolts on the fly 🖥️🔧 🔧 Why It Matters: Whether it's for automated manufacturing, assembly line optimization, or even robotics, this technology has massive potential for industries that depend on precision. 🔍 🤝 Collaboration & Learning: None of this would have been possible without the incredible teamwork of my peers Sathvika Anugu,RAMPRASAD D AVALAKKI, Pruthvi Asangi . Together, we tackled challenges from coding to deployment, sharing ideas and pushing the limits of what we thought was possible. If you’re curious about how AI and robotics can transform real-world industries, I’d love to connect and share more insights! 🌐 🚀 What’s Next? We’re excited about expanding this model to work in more complex environments and take on larger challenges. 💪 #MachineLearning #YOLOv8 #AI #Automation #Robotics #ROS #Ubuntu #ComputerVision #Teamwork #Innovation #Industry40 #DeepLearning #AIinIndustry

🎶🎶Gesture Recognition 🎶🎶 Just wrapped up an exciting PyCharm project! By using finger gestures, I can now control the volume of a video—thumbs up for volume up, index finger for volume down. The system analyzes my gestures and responds in real time. Excited about the potential of gesture-based tech for seamless, touch-free interaction! #GestureRecognition #PyCharm #Automation #TechInnovation #PythonProjects

👋Hello linkedin People!... 🙂Excited to share that I have successfully completed an object detection project using YOLO v8 on the Robo Platform! 🚀This project has been an incredible learning experience, allowing me to delve deep into the world of computer vision and object detection. I faced several challenges along the way, but the journey has been immensely rewarding. Special thanks to Satish Sir helped me to complete this task.. Looking forward to applying these skills to future projects and exploring more in the field of Al and machine learning! #AIMERS #AIMERSSOCIETY #ObjectDetection #YOLOv8 #RoboPlatform #Al