categorAI_img / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: categorAI_img
    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: 0.8378378378378378

categorAI_img

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: 0.7080
  • Accuracy: 0.8378

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9091 5 1.8872 0.3784
7.7979 1.9091 10 1.7777 0.6419
7.7979 2.9091 15 1.6224 0.6622
6.9519 3.9091 20 1.4667 0.6959
6.9519 4.9091 25 1.3353 0.7365
5.7562 5.9091 30 1.2522 0.7703
5.7562 6.9091 35 1.1617 0.7838
4.7446 7.9091 40 1.0967 0.7635
4.7446 8.9091 45 1.0362 0.7568
4.0655 9.9091 50 0.9349 0.8108
4.0655 10.9091 55 0.9393 0.7905
3.5041 11.9091 60 0.8859 0.7838
3.5041 12.9091 65 0.9039 0.7770
3.0788 13.9091 70 0.8123 0.8041
3.0788 14.9091 75 0.7946 0.8243
2.7461 15.9091 80 0.8003 0.8311
2.7461 16.9091 85 0.8101 0.7703
2.4988 17.9091 90 0.7111 0.8176
2.4988 18.9091 95 0.7439 0.8243
2.3122 19.9091 100 0.7542 0.7905
2.3122 20.9091 105 0.7323 0.8311
2.3408 21.9091 110 0.7175 0.8243
2.3408 22.9091 115 0.7652 0.8041
2.2846 23.9091 120 0.7211 0.8176
2.2846 24.9091 125 0.7080 0.8378

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1.post306
  • Datasets 3.2.0
  • Tokenizers 0.21.0