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vit-military-aircraft

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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: vit-base-beans
    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: 0.90272614622057

vit-base-beans

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: 0.3643
  • Accuracy: 0.9027

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: 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: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.5924 0.0620 100 3.5675 0.1927
3.0189 0.1239 200 3.0313 0.3047
2.5541 0.1859 300 2.5575 0.3956
2.114 0.2478 400 2.2332 0.4571
1.9624 0.3098 500 1.9455 0.5596
1.6749 0.3717 600 1.7370 0.5787
1.5852 0.4337 700 1.4947 0.6439
1.1875 0.4957 800 1.4151 0.6468
1.5114 0.5576 900 1.2709 0.6820
1.3122 0.6196 1000 1.1940 0.6939
1.0721 0.6815 1100 1.0757 0.7261
0.8249 0.7435 1200 0.9666 0.7576
0.7944 0.8055 1300 0.9101 0.7708
0.8032 0.8674 1400 0.9011 0.7691
0.7479 0.9294 1500 0.7409 0.8067
0.5997 0.9913 1600 0.7326 0.8110
0.5005 1.0533 1700 0.6769 0.8211
0.4107 1.1152 1800 0.6375 0.8374
0.4596 1.1772 1900 0.6302 0.8304
0.2544 1.2392 2000 0.5805 0.8400
0.2983 1.3011 2100 0.5480 0.8501
0.3214 1.3631 2200 0.5053 0.8683
0.2384 1.4250 2300 0.4929 0.8713
0.2397 1.4870 2400 0.4664 0.8742
0.3448 1.5489 2500 0.4690 0.8755
0.3129 1.6109 2600 0.4351 0.8843
0.1027 1.6729 2700 0.4311 0.8846
0.2086 1.7348 2800 0.4088 0.8897
0.1683 1.7968 2900 0.4133 0.8919
0.2767 1.8587 3000 0.3851 0.8964
0.1582 1.9207 3100 0.3703 0.9018
0.1421 1.9827 3200 0.3643 0.9027

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.19.1