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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-dmae-va-U5-42D
    results: []

vit-base-patch16-224-dmae-va-U5-42D

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

  • Loss: 1.0383
  • Accuracy: 0.55

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: 0.003
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 42

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9 7 1.0970 0.5167
1.3527 1.94 15 1.0383 0.55
1.3527 2.97 23 1.2351 0.4167
1.3013 4.0 31 1.3025 0.3333
1.3706 4.9 38 1.3800 0.2167
1.3706 5.94 46 1.4609 0.1833
1.4415 6.97 54 1.3718 0.4333
1.3602 8.0 62 1.3173 0.3167
1.3602 8.9 69 1.2827 0.4
1.3079 9.94 77 1.3167 0.3167
1.3247 10.97 85 1.2579 0.4
1.3247 12.0 93 1.3202 0.2
1.3102 12.9 100 1.2354 0.45
1.2807 13.94 108 1.3610 0.25
1.2807 14.97 116 1.2803 0.4
1.2774 16.0 124 1.3338 0.2167
1.2774 16.9 131 1.2549 0.35
1.2596 17.94 139 1.2693 0.3667
1.2413 18.97 147 1.3005 0.2167
1.2413 20.0 155 1.2299 0.4333
1.262 20.9 162 1.3454 0.2667
1.2261 21.94 170 1.2818 0.3167
1.2261 22.97 178 1.2498 0.4333
1.2405 24.0 186 1.3376 0.3167
1.2245 24.9 193 1.2595 0.3667
1.2245 25.94 201 1.3319 0.4
1.2034 26.97 209 1.2528 0.3833
1.1818 28.0 217 1.3656 0.3667
1.1818 28.9 224 1.2501 0.3833
1.1479 29.94 232 1.3241 0.3
1.1193 30.97 240 1.3803 0.3667
1.1193 32.0 248 1.2294 0.4167
1.1071 32.9 255 1.4134 0.5
1.1071 33.94 263 1.4123 0.3667
1.0429 34.97 271 1.2184 0.5
1.0528 36.0 279 1.3100 0.45
1.0528 36.9 286 1.3249 0.3833
1.0055 37.94 294 1.3051 0.5

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2