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FASHION-vision

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: fashion-MNIST-vision
    results: []

fashion-MNIST-vision

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3052
  • Accuracy: 0.9113

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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0436 1.0 375 1.0302 0.7701
0.6703 2.0 750 0.6180 0.8448
0.5315 3.0 1125 0.5132 0.8509
0.4074 4.0 1500 0.4185 0.8671
0.3998 5.0 1875 0.3854 0.8705
0.3436 6.0 2250 0.3563 0.8778
0.3501 7.0 2625 0.3156 0.888
0.3284 8.0 3000 0.3145 0.8932
0.3123 9.0 3375 0.3207 0.888
0.2989 10.0 3750 0.3059 0.8939
0.259 11.0 4125 0.2933 0.898
0.2115 12.0 4500 0.3067 0.8931
0.2928 13.0 4875 0.2869 0.8966
0.2398 14.0 5250 0.2865 0.8989
0.2187 15.0 5625 0.2955 0.9005
0.2335 16.0 6000 0.2814 0.8998
0.2165 17.0 6375 0.2863 0.8998
0.2092 18.0 6750 0.2912 0.9022
0.2002 19.0 7125 0.2769 0.9025
0.163 20.0 7500 0.2906 0.9029
0.1795 21.0 7875 0.2832 0.9065
0.1568 22.0 8250 0.2908 0.8972
0.1815 23.0 8625 0.2913 0.9055
0.158 24.0 9000 0.2926 0.9057
0.1672 25.0 9375 0.2810 0.9056
0.1846 26.0 9750 0.2894 0.9032
0.1599 27.0 10125 0.3073 0.9025
0.1547 28.0 10500 0.2990 0.9045
0.1342 29.0 10875 0.2938 0.9093
0.1594 30.0 11250 0.2949 0.9058
0.1582 31.0 11625 0.3076 0.9037
0.1453 32.0 12000 0.2888 0.9086
0.1643 33.0 12375 0.3031 0.9074
0.1064 34.0 12750 0.3045 0.9046
0.1661 35.0 13125 0.2968 0.909
0.1345 36.0 13500 0.3027 0.9105
0.1359 37.0 13875 0.3123 0.9069
0.1261 38.0 14250 0.3079 0.9073
0.1347 39.0 14625 0.3095 0.9095
0.1364 40.0 15000 0.3020 0.9083
0.108 41.0 15375 0.2934 0.9117
0.1269 42.0 15750 0.3050 0.9125
0.1187 43.0 16125 0.3144 0.9103
0.11 44.0 16500 0.3073 0.9072
0.113 45.0 16875 0.3125 0.9109
0.0935 46.0 17250 0.3088 0.9129
0.1287 47.0 17625 0.3085 0.9139
0.1186 48.0 18000 0.3069 0.9118
0.1353 49.0 18375 0.3205 0.9117
0.1122 50.0 18750 0.3052 0.9113

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.2
  • Datasets 2.19.0
  • Tokenizers 0.19.1