פיתוח בינה מלאכותית : איפה DATASET בחינם ? – רובוטרוניקס: מפתחת מעגלים ואלקטרוניקה ,מיקרובקרים , תוכנה ורובוטיקה: התקשר עכשיו 0506399001

Training set: 70% (840 images) for model training.
Validation set: 15% (180 images) for tuning and monitoring during training.
Test set: 15% (180 images) for final evaluation after training

פיתוח בינה מלאכותית : איפה DATASET בחינם ?

1200 images of planes : split the 1200 images like this:

Training Set (840 images):
- During training: The model learns by adjusting its weights based on this data. It's used in every epoch to minimize the loss function.
Validation Set (180 images):
- During training: Used to evaluate the model's performance after each epoch, helping to adjust hyperparameters or stop training early if overfitting is detected.
Test Set (180 images):
- After training: Used to assess the model's final performance on unseen data to provide an unbiased estimate of accuracy or other metrics.

Problem: If the CNN is trained to classify a single object per image (e.g., "plane" or "not plane"), having multiple planes in the same image could confuse the model, as it expects one dominant object.
Solution: In this case, you need to ensure that each image clearly represents a single class or switch to a more appropriate approach like multi-label classification if multiple planes are expected to be labeled as separate entities.

No Problem: If you're using the CNN for object detection (e.g., detecting multiple planes and their positions), the model can handle multiple objects. Algorithms like YOLO or Faster R-CNN are built to detect and localize multiple objects in one image.
Solution: In this case, you label each plane's bounding box and train the model to detect all objects within the image.

Solution: In segmentation tasks, you can label the boundaries of each plane, and the CNN will learn to identify and segment each one. For counting, you could design the model to recognize multiple instances and count the number of planes in each image.