TirathP / vit-base-patch16-224-finetuned-customData

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: -3
30-day runs: -10
Model's Last Updated: August 10 2023
image-classification

Introduction of vit-base-patch16-224-finetuned-customData

Model Details of vit-base-patch16-224-finetuned-customData

TirathP/vit-base-patch16-224-finetuned-customData

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:

  • Train Loss: 0.2775
  • Validation Loss: 0.3297
  • Validation Accuracy: 0.8571
  • Epoch: 19
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:

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32
Training results
Train Loss Validation Loss Validation Accuracy Epoch
1.1397 1.0223 0.5714 0
0.8312 0.8338 0.5714 1
0.7131 0.7099 0.5714 2
0.5754 0.6120 0.7143 3
0.4804 0.5374 0.7143 4
0.3934 0.4630 0.8571 5
0.4258 0.3979 0.8571 6
0.3739 0.3455 1.0 7
0.3143 0.2909 1.0 8
0.3113 0.2572 0.8571 9
0.3327 0.2623 0.8571 10
0.2227 0.2993 0.8571 11
0.2860 0.3299 0.8571 12
0.2081 0.3553 0.8571 13
0.2243 0.3360 0.8571 14
0.2246 0.2942 0.8571 15
0.2570 0.2131 0.8571 16
0.3173 0.1850 0.8571 17
0.1572 0.2134 0.8571 18
0.2775 0.3297 0.8571 19
Framework versions
  • Transformers 4.31.0
  • TensorFlow 2.12.0
  • Datasets 2.14.4
  • Tokenizers 0.13.3

Runs of TirathP vit-base-patch16-224-finetuned-customData on huggingface.co

4
Total runs
0
24-hour runs
-1
3-day runs
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7-day runs
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30-day runs

More Information About vit-base-patch16-224-finetuned-customData huggingface.co Model

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vit-base-patch16-224-finetuned-customData install

vit-base-patch16-224-finetuned-customData is an open source model from GitHub that offers a free installation service, and any user can find vit-base-patch16-224-finetuned-customData on GitHub to install. At the same time, huggingface.co provides the effect of vit-base-patch16-224-finetuned-customData install, users can directly use vit-base-patch16-224-finetuned-customData installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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