---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr_finetuned_cppe5
  results: []
datasets:
- rishitdagli/cppe-5
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Model Card for DETR Finetuned on CPPE-5

## Model Overview

This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on a custom dataset, likely focused on detecting personal protective equipment (PPE) items. The fine-tuning has optimized the model to recognize various PPE elements such as face shields, masks, gloves, and goggles.

The model is based on the DEtection TRansformer (DETR) architecture, leveraging a ResNet-50 backbone for feature extraction. This fine-tuned version retains DETR's core functionality, enabling object detection tasks but is specifically adjusted to detect items relevant to occupational safety or PPE.

## Model Performance

The model achieves the following metrics on its evaluation set:

- **Loss**: 1.2294
- **mAP** (mean Average Precision): 
  - Overall: 0.2366
  - 50 IoU threshold: 0.4852
  - 75 IoU threshold: 0.2032
  - Small objects: 0.1082
  - Medium objects: 0.2086
  - Large objects: 0.3408
- **mAR** (mean Average Recall):
  - At 1 detection: 0.2819
  - At 10 detections: 0.4463
  - At 100 detections: 0.4665
  - Small objects: 0.249
  - Medium objects: 0.4004
  - Large objects: 0.5893

For specific categories (face shields, gloves, goggles, masks), the precision and recall vary, with room for improvement, particularly for small objects like goggles.

## Intended Use and Limitations

### Intended Use
- Detecting personal protective equipment (PPE) in images or video streams.
- Monitoring workplace safety by ensuring proper usage of PPE items such as masks, gloves, face shields, and goggles.
- Suitable for industries like construction, healthcare, and manufacturing where PPE detection is critical for compliance and safety.

### Limitations
- The model may not generalize well to non-PPE items or general object detection tasks.
- Performance on small or occluded objects can be limited, as indicated by lower mAP and mAR scores for small objects.
- The model was trained on a dataset specific to PPE detection, so its performance on images outside of this domain might be inconsistent.

## Training and Evaluation Data

The dataset used for fine-tuning remains unspecified, but it appears to focus on personal protective equipment, such as face shields, masks, goggles, and gloves.

## Training Procedure

### Hyperparameters:
- **Learning rate**: 5e-05
- **Train batch size**: 8
- **Eval batch size**: 8
- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)
- **Learning rate scheduler**: Cosine decay
- **Number of epochs**: 30
- **Seed**: 42

The model was trained for 30 epochs with Adam optimization, using a learning rate of 5e-05 and cosine learning rate decay. The training was conducted with a batch size of 8 for both training and evaluation.

## Evaluation Results

The following are performance metrics captured during the training process across multiple epochs:

| Epoch | Validation Loss | mAP | mAP 50 | mAP 75 | mAR | Comments |
|-------|-----------------|-----|--------|--------|-----|----------|
| 1     | 2.1073          | 0.0518 | 0.1075 | 0.0423 | 0.2819 | Initial training |
| 5     | 1.6220          | 0.1223 | 0.2258 | 0.1115 | 0.4463 | Significant improvement |
| 10    | 1.5033          | 0.155  | 0.3265 | 0.1325 | 0.5032 | Stable performance |
| 20    | 1.2649          | 0.2211 | 0.4427 | 0.1952 | 0.5867 | Peak performance |
| 25    | 1.2347          | 0.2333 | 0.4831 | 0.1989 | 0.5966 | Final metrics |

## Limitations and Ethical Considerations

### Limitations:
- **Domain-specific**: The model performs well in PPE-related object detection but may not generalize to other tasks.
- **Bias**: If the dataset is skewed or limited, certain PPE items may be under-represented, leading to poorer performance for some categories.
- **Real-time Applications**: The model might not meet the latency requirements for real-time detection in high-throughput environments.

### Ethical Considerations:
- **Privacy**: Using this model in surveillance scenarios (e.g., workplaces) may raise concerns about employee privacy, especially if applied without clear consent.
- **Misuse**: Improper use of this model could lead to incorrect enforcement of safety regulations.

## Future Work

- **Dataset Improvements**: Expanding the dataset to include more diverse PPE items, environments, and object scales could improve model performance, especially for smaller objects.
- **Model Efficiency**: Further fine-tuning or model distillation may help make the model more suitable for real-time applications.