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urinary_carcinoma_classifier_g001

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
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urinary_carcinoma_classifier_g001 / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: urinary_carcinoma_classifier_g001
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:33]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8571428571428571

urinary_carcinoma_classifier_g001

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3828
  • Accuracy: 0.8571

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 1 0.6854 0.7143
No log 2.0 2 0.6759 0.5714
No log 3.0 3 0.6738 0.5714
No log 4.0 4 0.6571 0.5714
No log 5.0 5 0.6342 0.5714
No log 6.0 6 0.6339 0.5714
No log 7.0 7 0.5402 0.5714
No log 8.0 8 0.5827 0.5714
No log 9.0 9 0.5439 0.7143
0.2718 10.0 10 0.5553 0.7143
0.2718 11.0 11 0.4241 1.0
0.2718 12.0 12 0.5177 0.8571
0.2718 13.0 13 0.4088 0.8571
0.2718 14.0 14 0.4763 0.7143
0.2718 15.0 15 0.3164 1.0
0.2718 16.0 16 0.3087 1.0
0.2718 17.0 17 0.3457 0.8571
0.2718 18.0 18 0.2585 1.0
0.2718 19.0 19 0.3642 0.8571
0.1299 20.0 20 0.4421 0.7143
0.1299 21.0 21 0.3558 0.8571
0.1299 22.0 22 0.3611 0.8571
0.1299 23.0 23 0.5796 0.7143
0.1299 24.0 24 0.4137 0.8571
0.1299 25.0 25 0.4281 0.8571
0.1299 26.0 26 0.2066 1.0
0.1299 27.0 27 0.2251 1.0
0.1299 28.0 28 0.2459 1.0
0.1299 29.0 29 0.4450 0.8571
0.0743 30.0 30 0.3828 0.8571

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1