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my_awesome_food_model

Image Classification
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mobilevit
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metadata
library_name: transformers
license: other
base_model: apple/mobilevit-small
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: my_awesome_food_model
    results: []

my_awesome_food_model

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

  • Loss: 0.4411
  • Accuracy: 0.86

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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.2774 0.992 31 4.2776 0.563
4.2309 1.984 62 4.2272 0.616
4.1622 2.976 93 4.1229 0.646
4.045 4.0 125 3.9633 0.663
3.87 4.992 156 3.7407 0.68
3.6564 5.984 187 3.5054 0.694
3.4188 6.976 218 3.1871 0.681
3.0118 8.0 250 2.8682 0.682
2.6634 8.992 281 2.5058 0.668
2.3342 9.984 312 2.1583 0.687
2.0751 10.9760 343 1.8516 0.708
1.8887 12.0 375 1.6293 0.737
1.7637 12.992 406 1.5026 0.733
1.6451 13.984 437 1.3837 0.745
1.4845 14.9760 468 1.2238 0.765
1.3586 16.0 500 1.1391 0.775
1.2649 16.992 531 0.9872 0.784
1.1469 17.984 562 0.9524 0.77
1.0319 18.976 593 0.8425 0.797
0.9926 20.0 625 0.8079 0.791
0.9592 20.992 656 0.7261 0.808
0.8481 21.984 687 0.7273 0.799
0.8027 22.976 718 0.6501 0.807
0.8258 24.0 750 0.6499 0.818
0.7785 24.992 781 0.6178 0.83
0.7881 25.984 812 0.6305 0.827
0.7034 26.976 843 0.6201 0.829
0.705 28.0 875 0.5611 0.842
0.6981 28.992 906 0.5357 0.846
0.675 29.984 937 0.5622 0.844
0.6339 30.976 968 0.5188 0.847
0.611 32.0 1000 0.4712 0.879
0.6036 32.992 1031 0.5218 0.847
0.5909 33.984 1062 0.5201 0.84
0.5695 34.976 1093 0.4935 0.854
0.5752 36.0 1125 0.4752 0.858
0.5487 36.992 1156 0.5121 0.837
0.5538 37.984 1187 0.5111 0.852
0.5267 38.976 1218 0.4884 0.853
0.4907 40.0 1250 0.4921 0.859
0.4901 40.992 1281 0.5001 0.849
0.5134 41.984 1312 0.4409 0.865
0.5019 42.976 1343 0.4730 0.863
0.4642 44.0 1375 0.4619 0.865
0.4778 44.992 1406 0.4911 0.861
0.4521 45.984 1437 0.4471 0.855
0.473 46.976 1468 0.4661 0.864
0.4752 48.0 1500 0.4917 0.848
0.4582 48.992 1531 0.4685 0.862
0.4282 49.984 1562 0.4314 0.875
0.4344 50.976 1593 0.4270 0.878
0.4212 52.0 1625 0.4657 0.861
0.4245 52.992 1656 0.4608 0.857
0.4055 53.984 1687 0.4717 0.856
0.3802 54.976 1718 0.4428 0.871
0.4221 56.0 1750 0.4088 0.88
0.4254 56.992 1781 0.4310 0.869
0.3963 57.984 1812 0.4320 0.864
0.4375 58.976 1843 0.4404 0.876
0.3685 60.0 1875 0.4369 0.866
0.3911 60.992 1906 0.4491 0.861
0.3761 61.984 1937 0.4509 0.86
0.3703 62.976 1968 0.4468 0.861
0.3602 64.0 2000 0.4596 0.87
0.4034 64.992 2031 0.4232 0.86
0.3726 65.984 2062 0.4214 0.877
0.4187 66.976 2093 0.4509 0.868
0.3858 68.0 2125 0.4067 0.878
0.3933 68.992 2156 0.4295 0.879
0.3461 69.984 2187 0.4092 0.88
0.3909 70.976 2218 0.4518 0.862
0.3737 72.0 2250 0.4414 0.867
0.344 72.992 2281 0.4335 0.869
0.3403 73.984 2312 0.4470 0.863
0.3433 74.976 2343 0.4016 0.881
0.3292 76.0 2375 0.4565 0.861
0.3115 76.992 2406 0.4368 0.87
0.3498 77.984 2437 0.4516 0.862
0.3456 78.976 2468 0.3779 0.889
0.3284 80.0 2500 0.4441 0.865
0.3723 80.992 2531 0.4150 0.874
0.3269 81.984 2562 0.4491 0.864
0.3863 82.976 2593 0.4106 0.884
0.3376 84.0 2625 0.4367 0.87
0.3794 84.992 2656 0.4282 0.863
0.3498 85.984 2687 0.4185 0.877
0.293 86.976 2718 0.4207 0.87
0.3106 88.0 2750 0.4316 0.873
0.3061 88.992 2781 0.4254 0.874
0.3235 89.984 2812 0.4251 0.878
0.3182 90.976 2843 0.4247 0.873
0.3666 92.0 2875 0.4079 0.879
0.336 92.992 2906 0.4187 0.871
0.3262 93.984 2937 0.4461 0.868
0.3504 94.976 2968 0.4713 0.852
0.3106 96.0 3000 0.4610 0.863
0.2671 96.992 3031 0.4614 0.866
0.2929 97.984 3062 0.4278 0.872
0.353 98.976 3093 0.4134 0.872
0.3331 99.2 3100 0.4411 0.86

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.0
  • Datasets 3.0.1
  • Tokenizers 0.20.0