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felidae_klasifikasi

Image Classification
Transformers
TensorFlow
vit
generated_from_keras_callback
Inference Endpoints
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_keras_callback
model-index:
  - name: aditnnda/felidae_klasifikasi
    results: []

aditnnda/felidae_klasifikasi

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

  • Train Loss: 0.5782
  • Train Accuracy: 0.8361
  • Validation Loss: 0.5283
  • Validation Accuracy: 0.8361
  • 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Train Accuracy Validation Loss Validation Accuracy Epoch
1.5945 0.5574 1.5482 0.5574 0
1.5213 0.7541 1.4625 0.7541 1
1.4429 0.7049 1.3574 0.7049 2
1.3399 0.7869 1.2390 0.7869 3
1.2264 0.6721 1.1328 0.6721 4
1.1660 0.7869 1.0287 0.7869 5
1.0825 0.7377 0.9690 0.7377 6
1.0005 0.8197 0.8654 0.8197 7
0.9121 0.7869 0.8303 0.7869 8
0.8530 0.8525 0.7590 0.8525 9
0.8602 0.8361 0.7169 0.8361 10
0.8420 0.8197 0.6993 0.8197 11
0.7772 0.8689 0.6347 0.8689 12
0.7447 0.8689 0.6023 0.8689 13
0.7253 0.8197 0.6458 0.8197 14
0.6994 0.8361 0.6045 0.8361 15
0.6761 0.8361 0.6030 0.8361 16
0.5814 0.8197 0.5523 0.8197 17
0.5939 0.8689 0.5456 0.8689 18
0.5782 0.8361 0.5283 0.8361 19

Framework versions

  • Transformers 4.35.1
  • TensorFlow 2.14.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1