yolos-small-Abdomen_MRI

This model is a fine-tuned version of hustvl/yolos-small.

Model description

https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Abdomen%20MRIs%20Object%20Detection/Abdomen_MRI_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/abdomen-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: 15

Training results

Metric Name	IoU	Area	maxDets	Value
Average Precision (AP)	0.50:0.95	all	100	0.453
Average Precision (AP)	0.50	all	100	0.928
Average Precision (AP)	0.75	all	100	0.319
Average Precision (AP)	0.50:0.95	small	100	-1.000
Average Precision (AP)	0.50:0.95	medium	100	0.426
Average Precision (AP)	0.50:0.95	large	100	0.457
Average Recall (AR)	0.50:0.95	all	1	0.518
Average Recall (AR)	0.50:0.95	all	10	0.645
Average Recall (AR)	0.50:0.95	all	100	0.715
Average Recall (AR)	0.50:0.95	small	100	-1.000
Average Recall (AR)	0.50:0.95	medium	100	0.633
Average Recall (AR)	0.50:0.95	large	100	0.716

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.1
• 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.