yolos-small-Axial_MRIs

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/Axial%20MRIs/Axial_MRIs_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/axial-mri

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: 25

Training results

Metric Name	IoU	Area	maxDets	Metric Value
Average Precision (AP)	IoU=0.50:0.95	all	maxDets=100	0.284
Average Precision (AP)	IoU=0.50	all	maxDets=100	0.451
Average Precision (AP)	IoU=0.75	all	maxDets=100	0.351
Average Precision (AP)	IoU=0.50:0.95	small	maxDets=100	0.000
Average Precision (AP)	IoU=0.50:0.95	medium	maxDets=100	0.182
Average Precision (AP)	IoU=0.50:0.95	large	maxDets=100	0.663
Average Recall (AR)	IoU=0.50:0.95	all	maxDets=1	0.388
Average Recall (AR)	IoU=0.50:0.95	all	maxDets=10	0.524
Average Recall (AR)	IoU=0.50:0.95	all	maxDets=100	0.566
Average Recall (AR)	IoU=0.50:0.95	small	maxDets=100	0.000
Average Recall (AR)	IoU=0.50:0.95	medium	maxDets=100	0.502
Average Recall (AR)	IoU=0.50:0.95	large	maxDets=100	0.791

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.3
• Tokenizers 0.13.3