🔍YOLO 11 allows tasks like object detection, segmentation, and image classification.
👩💻Custom object detection training demonstrated in the tutorial using YOLO 11.
📦Instance segmentation on custom data showcased alongside model training.
⏲️Tutorial emphasizes reusing prior knowledge for custom training efficiency.
📝Demonstrates the importance of balanced data sets for accurate model training.
Where can I find the data for training the custom model?
—Data collection and preparation explained with a focus on labels and balance in the tutorial.
How can I access the YOLO 11 model for custom training?
—Tutorial guides on model selection and retrieval from Alternaetics platform.
What is the significance of choosing the right image size for training?
—Tutorial emphasizes the impact of image dimensions on model performance.
Is GPU necessary for model training, and how can it be accessed for free?
—Instructions provided on GPU utilization and access through Google Colab for efficient training.
Can I use pre-trained models with YOLO 11, and how effective are they?
—Explanation on leveraging pre-trained models like Nano, Small, and Medium for improved training outcomes.
05:00Introduction to YOLO 11 for image classification, showcasing varied tasks possible.
09:00Demonstration on custom training setup, including data collection and model selection.
18:00Importance of balanced data sets and label structuring discussed for optimal training.
25:00Training process explained with details on model download, data loading, and epoch settings.
35:00Model evaluation and performance analysis showcased post-training, emphasizing accuracy and loss metrics.
42:00Inference demonstration with model loading, image testing, and result interpretation.
50:00Guidance on leveraging GPU for improved training efficiency and accessing free resources like Alternaetics.
01:00:00Review of key steps in custom image classification using YOLO 11, highlighting model training and inference procedures.
