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yolos-tiny-Hard_Hat_Detection

This model is a fine-tuned version of 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
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