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
