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metadata
license: apache-2.0
base_model: hustvl/yolos-small
tags:
  - generated_from_trainer
  - Blood Cells
  - biology
  - medical
datasets:
  - blood-cell-object-detection
model-index:
  - name: yolos-small-Blood_Cell_Object_Detection
    results: []
language:
  - en
pipeline_tag: object-detection

yolos-small-Blood_Cell_Object_Detection

This model is a fine-tuned version of hustvl/yolos-small on the blood-cell-object-detection dataset.

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/Blood%20Cell%20Object%20Detection/Blood_Cell_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/keremberke/blood-cell-object-detection

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 0.344
Average Precision (AP) IoU=0.50 all maxDets=100 0.579
Average Precision (AP) IoU=0.75 all maxDets=100 0.374
Average Precision (AP) IoU=0.50:0.95 small maxDets=100 0.097
Average Precision (AP) IoU=0.50:0.95 medium maxDets=100 0.258
Average Precision (AP) IoU=0.50:0.95 large maxDets=100 0.224
Average Recall (AR) IoU=0.50:0.95 all maxDets=1 0.210
Average Recall (AR) IoU=0.50:0.95 all maxDets=10 0.376
Average Recall (AR) IoU=0.50:0.95 all maxDets=100 0.448
Average Recall (AR) IoU=0.50:0.95 small maxDets=100 0.108
Average Recall (AR) IoU=0.50:0.95 medium maxDets=100 0.375
Average Recall (AR) IoU=0.50:0.95 large maxDets=100 0.448

Framework versions

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