Boost YOLOv26 Detection Accuracy

My Kaggle notebook already trains a pair of YOLOv26 models that spot and product-type objects, but their best validation mAP hovers below the 85 % . I want that number pushed higher, with a clear focus on correctly identifying the type of each object rather than merely finding every box or lowering false positives.

Feel free to experiment with hyper-parameter tuning, better augmentation, anchor re-calculation, or lightweight ensembling—whatever lifts classification performance—as long as everything runs end-to-end inside Kaggle and can be reproduced from a clean runtime.

When you are done I need:
• An updated Kaggle notebook with concise comments showing every change you made.
• The final trained weights for each model .
• Clear evaluation graphics: loss/precision-recall curves, confusion matrix, and the mAP figures proving the ≥85 % goal.
• A small Python script (OpenCV is fine) that loads the exported weights and performs real-time inference from a webcam.

Keep the code tidy, name the checkpoints sensibly, and note any extra libraries you add beyond the standard Kaggle image stack. If you can beat the target while maintaining reasonable inference speed, that’s a plus.