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cifar10_m

Image Classification
Transformers
TensorFlow
mobilevit
generated_from_keras_callback
Inference Endpoints
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cifar10_m / README.md
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metadata
license: other
base_model: apple/mobilevit-xx-small
tags:
  - generated_from_keras_callback
model-index:
  - name: hafizurUMaine/cifar10_m
    results: []

hafizurUMaine/cifar10_m

This model is a fine-tuned version of apple/mobilevit-xx-small on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0748
  • Train Accuracy: 0.9743
  • Validation Loss: 0.6597
  • Validation Accuracy: 0.8575
  • Epoch: 49

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': 400000, '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
5.5748 0.1482 3.1655 0.4160 0
2.4468 0.5135 1.7772 0.6195 1
1.5927 0.6389 1.3152 0.6770 2
1.2333 0.7001 1.1226 0.7265 3
1.0094 0.7334 0.9668 0.7490 4
0.8748 0.7591 0.9140 0.7510 5
0.7714 0.7846 0.7881 0.7845 6
0.6977 0.7999 0.8075 0.7745 7
0.6524 0.8096 0.8417 0.7675 8
0.5904 0.8254 0.7763 0.7850 9
0.5525 0.8321 0.7367 0.7955 10
0.5083 0.8459 0.7343 0.7990 11
0.4695 0.8559 0.6768 0.8075 12
0.4432 0.8615 0.6830 0.8095 13
0.4125 0.8704 0.6891 0.7980 14
0.3995 0.875 0.6482 0.8155 15
0.3723 0.8781 0.6653 0.8095 16
0.3505 0.8859 0.6268 0.8195 17
0.3390 0.8906 0.6243 0.8205 18
0.3132 0.8967 0.6338 0.8255 19
0.2879 0.9071 0.5879 0.8380 20
0.2845 0.9066 0.6004 0.8320 21
0.2578 0.9141 0.6228 0.8320 22
0.2521 0.9178 0.6208 0.8295 23
0.2375 0.9258 0.6051 0.8410 24
0.2226 0.9243 0.6138 0.8395 25
0.2139 0.9298 0.5651 0.8455 26
0.2094 0.9302 0.5881 0.8470 27
0.1925 0.9385 0.6298 0.8390 28
0.1806 0.9399 0.5982 0.8450 29
0.1758 0.9401 0.6139 0.8435 30
0.1630 0.9449 0.6105 0.8430 31
0.1566 0.9449 0.5953 0.8490 32
0.1423 0.9531 0.6246 0.8440 33
0.1378 0.9545 0.6249 0.8500 34
0.1379 0.9553 0.6625 0.8415 35
0.1305 0.9551 0.6035 0.8575 36
0.1253 0.9581 0.6503 0.8490 37
0.1149 0.9607 0.5882 0.8585 38
0.1026 0.9672 0.6130 0.8530 39
0.1019 0.9660 0.6373 0.8525 40
0.1038 0.9645 0.6197 0.8570 41
0.0938 0.9685 0.6239 0.8545 42
0.0910 0.9688 0.6439 0.8590 43
0.0869 0.9711 0.5812 0.8640 44
0.0818 0.9726 0.6692 0.8565 45
0.0695 0.9799 0.6652 0.8585 46
0.0756 0.9765 0.6584 0.8570 47
0.0669 0.9797 0.6542 0.8610 48
0.0748 0.9743 0.6597 0.8575 49

Framework versions

  • Transformers 4.37.2
  • TensorFlow 2.15.0
  • Datasets 2.16.1
  • Tokenizers 0.15.1