PranavPadampada/multi-label-object-classification

Multi-Label Object Classification using ResNet

Model Description

ResNet18 and ResNet50 models fine-tuned for multi-label object classification, capable of detecting 12 objects simultaneously in a single image.

Classes

backpack, book, bottle, calculator, chair, clock, desk, keychain, laptop, paper, pen, phone

Usage

Download all 10 .pth files and use with the inference script from the GitHub repository.

Model Files

• resnet18_fold1.pth through resnet18_fold5.pth — ResNet18 ensemble
• resnet50_fold1.pth through resnet50_fold5.pth — ResNet50 ensemble

Performance

Model	Exact Match	Micro F1	Mean IOU
ResNet18	52.29%	76.92%	0.7200
ResNet50	68.78%	86.18%	0.8301

Training

• 5-Fold Stratified Cross Validation
• Test Time Augmentation (TTA) with 10 augmented views
• Optimal prediction threshold: 0.40