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metadata
license: apache-2.0
base_model: google/vit-large-patch32-384
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-large-patch32-384-finetuned-galaxy10-decals
    results: []

vit-large-patch32-384-finetuned-galaxy10-decals

This model is a fine-tuned version of google/vit-large-patch32-384 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6766
  • Accuracy: 0.8371
  • Precision: 0.8374
  • Recall: 0.8371
  • F1: 0.8357

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: 0.0001
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.3342 0.99 31 1.0491 0.6313 0.6077 0.6313 0.6052
0.7979 1.98 62 0.6901 0.7672 0.7717 0.7672 0.7652
0.7197 2.98 93 0.6200 0.7785 0.7716 0.7785 0.7705
0.6321 4.0 125 0.5693 0.8061 0.8035 0.8061 0.7957
0.5768 4.99 156 0.5501 0.8112 0.8213 0.8112 0.8134
0.5173 5.98 187 0.5165 0.8213 0.8306 0.8213 0.8202
0.4781 6.98 218 0.5220 0.8106 0.8161 0.8106 0.8090
0.451 8.0 250 0.5133 0.8185 0.8227 0.8185 0.8153
0.4373 8.99 281 0.5118 0.8303 0.8325 0.8303 0.8288
0.3826 9.98 312 0.5280 0.8258 0.8269 0.8258 0.8243
0.378 10.98 343 0.5477 0.8174 0.8156 0.8174 0.8142
0.3509 12.0 375 0.5437 0.8281 0.8292 0.8281 0.8244
0.3358 12.99 406 0.5627 0.8258 0.8268 0.8258 0.8241
0.3027 13.98 437 0.5558 0.8326 0.8341 0.8326 0.8310
0.3027 14.98 468 0.5703 0.8326 0.8358 0.8326 0.8295
0.2786 16.0 500 0.5791 0.8281 0.8268 0.8281 0.8249
0.2379 16.99 531 0.5864 0.8275 0.8264 0.8275 0.8251
0.2426 17.98 562 0.5984 0.8320 0.8320 0.8320 0.8305
0.2325 18.98 593 0.6217 0.8264 0.8281 0.8264 0.8252
0.2208 20.0 625 0.6166 0.8258 0.8230 0.8258 0.8236
0.2196 20.99 656 0.6308 0.8286 0.8280 0.8286 0.8259
0.2077 21.98 687 0.6242 0.8326 0.8307 0.8326 0.8305
0.2048 22.98 718 0.6801 0.8275 0.8303 0.8275 0.8263
0.1886 24.0 750 0.6615 0.8264 0.8280 0.8264 0.8256
0.2007 24.99 781 0.6847 0.8275 0.8280 0.8275 0.8267
0.1815 25.98 812 0.6669 0.8326 0.8311 0.8326 0.8305
0.1958 26.98 843 0.6766 0.8371 0.8374 0.8371 0.8357
0.1806 28.0 875 0.6679 0.8360 0.8353 0.8360 0.8342
0.1835 28.99 906 0.6767 0.8348 0.8334 0.8348 0.8328
0.1796 29.76 930 0.6787 0.8343 0.8336 0.8343 0.8326

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1