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README.md
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model-index:
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- name: detr_finetuned_cppe5
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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#
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| 1.153 | 30.0 | 3210 | 1.2294 | 0.2366 | 0.4852 | 0.2032 | 0.1082 | 0.2086 | 0.3408 | 0.2819 | 0.4463 | 0.4665 | 0.249 | 0.4004 | 0.5893 | 0.5966 | 0.7461 | 0.1093 | 0.3645 | 0.1371 | 0.3865 | 0.0739 | 0.4417 | 0.266 | 0.3937 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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model-index:
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- name: detr_finetuned_cppe5
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results: []
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datasets:
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- rishitdagli/cppe-5
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Model Card for DETR Finetuned on CPPE-5
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## Model Overview
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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.
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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.
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## Model Performance
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The model achieves the following metrics on its evaluation set:
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- **Loss**: 1.2294
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- **mAP** (mean Average Precision):
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- Overall: 0.2366
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- 50 IoU threshold: 0.4852
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- 75 IoU threshold: 0.2032
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- Small objects: 0.1082
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- Medium objects: 0.2086
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- Large objects: 0.3408
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- **mAR** (mean Average Recall):
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- At 1 detection: 0.2819
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- At 10 detections: 0.4463
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- At 100 detections: 0.4665
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- Small objects: 0.249
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- Medium objects: 0.4004
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- Large objects: 0.5893
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For specific categories (face shields, gloves, goggles, masks), the precision and recall vary, with room for improvement, particularly for small objects like goggles.
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## Intended Use and Limitations
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### Intended Use
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- Detecting personal protective equipment (PPE) in images or video streams.
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- Monitoring workplace safety by ensuring proper usage of PPE items such as masks, gloves, face shields, and goggles.
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- Suitable for industries like construction, healthcare, and manufacturing where PPE detection is critical for compliance and safety.
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### Limitations
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- The model may not generalize well to non-PPE items or general object detection tasks.
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- Performance on small or occluded objects can be limited, as indicated by lower mAP and mAR scores for small objects.
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- The model was trained on a dataset specific to PPE detection, so its performance on images outside of this domain might be inconsistent.
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## Training and Evaluation Data
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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.
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## Training Procedure
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### Hyperparameters:
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- **Learning rate**: 5e-05
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- **Train batch size**: 8
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- **Eval batch size**: 8
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- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)
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- **Learning rate scheduler**: Cosine decay
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- **Number of epochs**: 30
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- **Seed**: 42
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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.
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## Evaluation Results
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The following are performance metrics captured during the training process across multiple epochs:
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| Epoch | Validation Loss | mAP | mAP 50 | mAP 75 | mAR | Comments |
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|-------|-----------------|-----|--------|--------|-----|----------|
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| 1 | 2.1073 | 0.0518 | 0.1075 | 0.0423 | 0.2819 | Initial training |
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| 5 | 1.6220 | 0.1223 | 0.2258 | 0.1115 | 0.4463 | Significant improvement |
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| 10 | 1.5033 | 0.155 | 0.3265 | 0.1325 | 0.5032 | Stable performance |
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| 20 | 1.2649 | 0.2211 | 0.4427 | 0.1952 | 0.5867 | Peak performance |
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| 25 | 1.2347 | 0.2333 | 0.4831 | 0.1989 | 0.5966 | Final metrics |
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## Limitations and Ethical Considerations
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### Limitations:
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- **Domain-specific**: The model performs well in PPE-related object detection but may not generalize to other tasks.
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- **Bias**: If the dataset is skewed or limited, certain PPE items may be under-represented, leading to poorer performance for some categories.
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- **Real-time Applications**: The model might not meet the latency requirements for real-time detection in high-throughput environments.
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### Ethical Considerations:
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- **Privacy**: Using this model in surveillance scenarios (e.g., workplaces) may raise concerns about employee privacy, especially if applied without clear consent.
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- **Misuse**: Improper use of this model could lead to incorrect enforcement of safety regulations.
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## Future Work
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- **Dataset Improvements**: Expanding the dataset to include more diverse PPE items, environments, and object scales could improve model performance, especially for smaller objects.
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- **Model Efficiency**: Further fine-tuning or model distillation may help make the model more suitable for real-time applications.
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