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metadata
license: apache-2.0
base_model: hustvl/yolos-small
tags:
  - generated_from_trainer
  - medical
model-index:
  - name: yolos-small-Axial_MRIs
    results: []
datasets:
  - Francesco/axial-mri
language:
  - en
pipeline_tag: object-detection

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