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
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