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End of training
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metadata
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: deit-base-distilled-patch16-224-hasta-55-fold4
    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: 0.6666666666666666

deit-base-distilled-patch16-224-hasta-55-fold4

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

  • Loss: 1.0211
  • Accuracy: 0.6667

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: 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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.5714 1 1.1563 0.4167
No log 1.7143 3 1.1167 0.3611
No log 2.8571 5 1.3251 0.3611
No log 4.0 7 1.1401 0.3611
No log 4.5714 8 1.0346 0.5278
1.1327 5.7143 10 1.0377 0.4444
1.1327 6.8571 12 1.0117 0.5556
1.1327 8.0 14 1.0721 0.3889
1.1327 8.5714 15 1.0312 0.4167
1.1327 9.7143 17 1.0228 0.5
1.1327 10.8571 19 0.9868 0.5556
0.9623 12.0 21 1.1119 0.5
0.9623 12.5714 22 1.1094 0.5
0.9623 13.7143 24 0.9811 0.6389
0.9623 14.8571 26 1.0888 0.4722
0.9623 16.0 28 1.0576 0.5833
0.9623 16.5714 29 1.0583 0.6389
0.8158 17.7143 31 1.0731 0.5833
0.8158 18.8571 33 0.9776 0.5833
0.8158 20.0 35 1.0211 0.6667
0.8158 20.5714 36 1.0035 0.6111
0.8158 21.7143 38 0.9845 0.5556
0.653 22.8571 40 1.0856 0.6389
0.653 24.0 42 1.0787 0.6111
0.653 24.5714 43 1.0536 0.5833
0.653 25.7143 45 1.0995 0.5833
0.653 26.8571 47 1.1207 0.5833
0.653 28.0 49 1.1610 0.5833
0.5371 28.5714 50 1.1164 0.6111
0.5371 29.7143 52 1.1101 0.5833
0.5371 30.8571 54 1.1040 0.5556
0.5371 32.0 56 1.0995 0.5833
0.5371 32.5714 57 1.1076 0.5833
0.5371 33.7143 59 1.1530 0.5833
0.4039 34.8571 61 1.1780 0.5556
0.4039 36.0 63 1.1379 0.6111
0.4039 36.5714 64 1.1324 0.6111
0.4039 37.7143 66 1.1546 0.5556
0.4039 38.8571 68 1.1910 0.5556
0.348 40.0 70 1.1487 0.5833
0.348 40.5714 71 1.1367 0.5833
0.348 41.7143 73 1.1627 0.5833
0.348 42.8571 75 1.2421 0.5833
0.348 44.0 77 1.3073 0.5556
0.348 44.5714 78 1.2773 0.5556
0.321 45.7143 80 1.2160 0.5833
0.321 46.8571 82 1.2220 0.5833
0.321 48.0 84 1.2168 0.5833
0.321 48.5714 85 1.2190 0.5833
0.321 49.7143 87 1.2255 0.5833
0.321 50.8571 89 1.2174 0.5833
0.3168 52.0 91 1.2152 0.5833
0.3168 52.5714 92 1.2308 0.5833
0.3168 53.7143 94 1.2728 0.5556
0.3168 54.8571 96 1.3121 0.5556
0.3168 56.0 98 1.3350 0.5556
0.3168 56.5714 99 1.3354 0.5556
0.2876 57.1429 100 1.3346 0.5556

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1