| --- |
| license: apache-2.0 |
| base_model: hustvl/yolos-tiny |
| tags: |
| - generated_from_trainer |
| - Workplace Safety |
| - Safety |
| datasets: |
| - hard-hat-detection |
| model-index: |
| - name: yolos-tiny-Hard_Hat_Detection |
| results: [] |
| language: |
| - en |
| pipeline_tag: object-detection |
| --- |
| |
| # yolos-tiny-Hard_Hat_Detection |
|
|
| This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the hard-hat-detection dataset. |
|
|
| ## Model description |
|
|
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Hard%20Hat%20Detection/Hard_Hat_Object_Detection_YOLOS.ipynb |
|
|
| ## Intended uses & limitations |
|
|
| This model is intended to demonstrate my ability to solve a complex problem using technology. |
|
|
| ## Training and evaluation data |
|
|
| Dataset Source: https://huggingface.co/datasets/keremberke/hard-hat-detection |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
|
|
| ### Training results |
|
|
| | Metric Name | IoU | Area| maxDets | Metric Value | |
| |:-----:|:-----:|:-----:|:-----:|:-----:| |
| | Average Precision (AP)| IoU=0.50:0.95 | all | maxDets=100 | 0.346 | |
| | Average Precision (AP)| IoU=0.50 | all | maxDets=100 | 0.747 | |
| | Average Precision (AP)| IoU=0.75 | all | maxDets=100 | 0.275 | |
| | Average Precision (AP)| IoU=0.50:0.95 | small | maxDets=100 | 0.128 | |
| | Average Precision (AP)| IoU=0.50:0.95 | medium | maxDets=100 | 0.343 | |
| | Average Precision (AP)| IoU=0.50:0.95 | large | maxDets=100 | 0.521 | |
| | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=1 | 0.188 | |
| | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=10 | 0.484 | |
| | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=100 | 0.558 | |
| | Average Recall (AR)| IoU=0.50:0.95 | small | maxDets=100 | 0.320 | |
| | Average Recall (AR)| IoU=0.50:0.95 | medium | maxDets=100 | 0.538 | |
| | Average Recall (AR)| IoU=0.50:0.95 | large | maxDets=100 | 0.743 | |
|
|
| ### Framework versions |
|
|
| - Transformers 4.31.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.3 |
| - Tokenizers 0.13.3 |
|
|
|
|
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
|
|
|
|
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
