---
license: mit
language:
- en
tags:
- image-classification
- multi-label
- resnet
- pytorch
---
# 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](https://github.com/pranav1233/multi-label-object-classification).

## 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