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dit_base

Image Classification
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dit_base / README.md
Ryan CHan
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metadata
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dit_base
    results: []

dit_base

This model is a fine-tuned version of microsoft/dit-base on the davanstrien/leicester_loaded_annotations dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4527
  • Accuracy: 0.8190

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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.89 6 1.7452 0.4095
1.8958 1.89 12 1.6185 0.4286
1.8958 2.89 18 1.4731 0.4857
1.8466 3.89 24 1.3459 0.5524
1.445 4.89 30 1.1766 0.5810
1.445 5.89 36 1.0902 0.6381
1.2077 6.89 42 0.9331 0.6762
1.2077 7.89 48 0.8431 0.6762
1.0254 8.89 54 0.8657 0.6857
0.8275 9.89 60 0.6801 0.7429
0.8275 10.89 66 0.6699 0.7810
0.8063 11.89 72 0.6296 0.7524
0.8063 12.89 78 0.5498 0.7905
0.7127 13.89 84 0.4974 0.8381
0.6356 14.89 90 0.6715 0.7619
0.6356 15.89 96 0.4602 0.8095
0.6438 16.89 102 0.4886 0.8095
0.6438 17.89 108 0.4332 0.8
0.5329 18.89 114 0.4197 0.8095
0.4932 19.89 120 0.4168 0.8190
0.4932 20.89 126 0.4691 0.8
0.4861 21.89 132 0.4263 0.8476
0.4861 22.89 138 0.4464 0.8190
0.4935 23.89 144 0.4857 0.7905
0.433 24.89 150 0.4873 0.7810
0.433 25.89 156 0.4641 0.8095
0.4289 26.89 162 0.5316 0.8
0.4289 27.89 168 0.3389 0.8571
0.4204 28.89 174 0.4272 0.8
0.3668 29.89 180 0.3493 0.8667
0.3668 30.89 186 0.3861 0.8571
0.4101 31.89 192 0.4216 0.8381
0.4101 32.89 198 0.4258 0.8190
0.3614 33.89 204 0.4409 0.8571
0.3267 34.89 210 0.4475 0.8190
0.3267 35.89 216 0.4316 0.8190
0.3423 36.89 222 0.4095 0.8381
0.3423 37.89 228 0.4671 0.8286
0.3325 38.89 234 0.3994 0.8286
0.3326 39.89 240 0.5004 0.8190
0.3326 40.89 246 0.4103 0.8381
0.2964 41.89 252 0.4469 0.8286
0.2964 42.89 258 0.4774 0.8286
0.3435 43.89 264 0.3843 0.8381
0.3146 44.89 270 0.3710 0.8667
0.3146 45.89 276 0.3392 0.8667
0.3168 46.89 282 0.3597 0.8667
0.3168 47.89 288 0.4143 0.8381
0.3081 48.89 294 0.3579 0.8571
0.3103 49.89 300 0.4527 0.8190

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1
  • Datasets 2.7.1
  • Tokenizers 0.13.1