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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8045685279187818
language:
  - en
pipeline_tag: image-classification

deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification

This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224.

It achieves the following results on the evaluation set:

  • Loss: 1.8587
  • Accuracy: 0.8046
  • Weighted f1: 0.7749
  • Micro f1: 0.8046
  • Macro f1: 0.7814
  • Weighted recall: 0.8046
  • Micro recall: 0.8046
  • Macro recall: 0.7996
  • Weighted precision: 0.8567
  • Micro precision: 0.8046
  • Macro precision: 0.8710

Model Description

Click here for the code that I used to create this model This project is part of a comparison of seventeen (17) transformers. Click here to see the README markdown file for the full project

Intended Uses & Limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training & Evaluation Data

Brain Tumor Image Classification Dataset

Sample Images

Class Distribution of Training Dataset

Class Distribution of Evaluation Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted f1 Micro f1 Macro f1 Weighted recall Micro recall Macro recall Weighted precision Micro precision Macro precision
1.6561 1.0 180 1.5974 0.7792 0.7454 0.7792 0.7524 0.7792 0.7792 0.7722 0.8318 0.7792 0.8488
1.6561 2.0 360 1.7614 0.7944 0.7575 0.7944 0.7633 0.7944 0.7944 0.7896 0.8458 0.7944 0.8582
0.172 3.0 540 1.8587 0.8046 0.7749 0.8046 0.7814 0.8046 0.8046 0.7996 0.8567 0.8046 0.8710

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.13.3