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urinary_carcinoma_classifier_g002

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
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urinary_carcinoma_classifier_g002 / 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_g002
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:63]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9230769230769231

urinary_carcinoma_classifier_g002

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: 0.3544
  • Accuracy: 0.9231

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 0.6814 0.5385
No log 2.0 2 0.6743 0.6923
No log 3.0 3 0.6449 0.7692
No log 4.0 4 0.6149 0.7692
No log 5.0 5 0.5980 0.7692
No log 6.0 6 0.5855 0.7692
No log 7.0 7 0.5663 0.7692
No log 8.0 8 0.5675 0.7692
No log 9.0 9 0.5530 0.7692
0.637 10.0 10 0.5246 0.8462
0.637 11.0 11 0.5135 0.7692
0.637 12.0 12 0.5296 0.8462
0.637 13.0 13 0.5340 0.8462
0.637 14.0 14 0.4781 0.9231
0.637 15.0 15 0.4870 0.8462
0.637 16.0 16 0.4701 0.8462
0.637 17.0 17 0.4521 1.0
0.637 18.0 18 0.4266 0.9231
0.637 19.0 19 0.4220 0.9231
0.4474 20.0 20 0.3837 0.9231
0.4474 21.0 21 0.4257 0.8462
0.4474 22.0 22 0.4093 0.9231
0.4474 23.0 23 0.4019 1.0
0.4474 24.0 24 0.4578 0.8462
0.4474 25.0 25 0.3932 1.0
0.4474 26.0 26 0.3838 1.0
0.4474 27.0 27 0.3627 1.0
0.4474 28.0 28 0.3862 0.9231
0.4474 29.0 29 0.3624 0.9231
0.3102 30.0 30 0.3544 0.9231

Framework versions

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