yolos-small-Wall_Damage
This model is a fine-tuned version of hustvl/yolos-small.
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/Trained%2C%20But%20to%20Standard/Wall%20Damage%20Object%20Detection/Wall_Damage_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/Francesco/wall-damage
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: 40
Training results
| Metric Name | IoU | Area | maxDets | Metric Value |
|---|---|---|---|---|
| Average Precision (AP) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.241 |
| Average Precision (AP) | IoU=0.50 | area= all | maxDets=100 | 0.400 |
| Average Precision (AP) | IoU=0.75 | area= all | maxDets=100 | 0.231 |
| Average Precision (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | -1.000 |
| Average Precision (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | -1.000 |
| Average Precision (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.241 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 1 | 0.488 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 10 | 0.579 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.621 |
| Average Recall (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | -1.000 |
| Average Recall (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | -1.000 |
| Average Recall (AR) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.621 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
