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SAM 3 for Video Concept Aware Segmentation and Tracking: Text-Prompt Video Tracking
Read the full tutorial here: https://pyimg.co/luxfd -
SAM 3 for Video Concept Aware Segmentation and Tracking: Real-Time Text-Prompt Tracking (Webcam)
Read the full tutorial here: https://pyimg.co/luxfd -
SAM 3 for Video Concept Aware Segmentation and Tracking: Single-Click Object Tracking
Read the full tutorial here: https://pyimg.co/luxfd -
SAM 3 for Video Concept Aware Segmentation and Tracking: Multi-Click Object Tracking
Read the full tutorial here: https://pyimg.co/luxfd -
Advanced SAM 3: Multi Modal Prompting and Interactive Segmentation
Read the full tutorial here: https://pyimg.co/5c4ag -
Grounded SAM 2: From Open Set Detection to Segmentation and Tracking
Read the full tutorial here: https://pyimg.co/flutd -
Grounding DINO: Open Vocabulary Object Detection on Videos
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Build a VLC Playlist Generator with SmolVLM for Video Highlight Tagging
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Running SmolVLM Locally in Your Browser with Transformers.js
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Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset - Video Input Demo
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Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset - Image Input Demo
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MLOps with Weights & Biases | PyImageSearch | Live learning
MLOps with Weights & Biases is a brand new and completely FREE course from our recent collaboration with Weights and Biases.
Enroll Now: https://pyimagesearch.mykajabi.com/offers/LQSsX59C/checkout
In this live stream, we are joined by course instructor Thomas Capelle. Thomas Capelle is a Machine Learning engineer at Weights and Biases who works on the Growth Team. He is responsible for keeping the wandb/examples repository live and up to date. He also builds content on ML-OPS, application of wandb to industry and fun deep learning in general. Previously he was using deep learning to solve short-term forecasting for solar energy at Steady Sun. So, he has a background in Urban Planning, Combinatorial Optimization, Transportation Economics and Applied Math.
The MLOps course offered by Weights & Biases is designed to provide a comprehensive understanding of implementing effective machine learning operations using their advanced platform. The course focuses on leveraging Weights & Biases' product for experiment tracking and management, utilizing a real-world lemon quality dataset as a practical example.
Throughout the course, participants learn how to seamlessly integrate Weights and Biases' toolset into their machine learning workflow, from data preprocessing to model training and deployment. The lemon quality dataset serves as a relevant case study, allowing learners to explore different aspects of MLOps, such as data versioning, hyperparameter tuning, model evaluation, and collaboration among team members.
Enroll Now: https://pyimagesearch.mykajabi.com/offers/LQSsX59C/checkout
