metadata

license: apache-2.0
base_model: hustvl/yolos-small
tags:
  - generated_from_trainer
  - medical
  - biology
model-index:
  - name: yolos-small-Abdomen_MRI
    results: []
datasets:
  - Francesco/abdomen-mri
language:
  - en
metrics:
  - mean_iou
pipeline_tag: object-detection

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