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resnet50-cocoa

Image Classification
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resnet
vision
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metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet50-cocoa
    results: []

resnet50-cocoa

This model is a fine-tuned version of microsoft/resnet-50 on the SemilleroCV/Cocoa-dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3381
  • Accuracy: 0.8917

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 1337
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 100.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3793 1.0 196 1.4452 0.8628
0.9417 2.0 392 1.0832 0.8628
0.8546 3.0 588 0.7324 0.8628
0.6067 4.0 784 0.5761 0.8628
0.5583 5.0 980 0.5221 0.8628
0.6819 6.0 1176 0.4618 0.8628
0.4154 7.0 1372 0.4545 0.8628
0.4997 8.0 1568 0.4556 0.8628
0.6623 9.0 1764 0.4483 0.8628
0.8141 10.0 1960 0.4494 0.8628
0.5514 11.0 2156 0.4437 0.8628
0.6831 12.0 2352 0.4407 0.8664
0.2799 13.0 2548 0.4459 0.8700
0.451 14.0 2744 0.4313 0.8809
0.3901 15.0 2940 0.4340 0.8845
0.4778 16.0 3136 0.4219 0.8845
0.5531 17.0 3332 0.4304 0.8845
0.4904 18.0 3528 0.4429 0.8845
0.5398 19.0 3724 0.4144 0.8917
0.8024 20.0 3920 0.4253 0.8881
0.7022 21.0 4116 0.4232 0.8917
0.3868 22.0 4312 0.4167 0.8917
0.4075 23.0 4508 0.3917 0.8917
0.3873 24.0 4704 0.4269 0.8881
0.2382 25.0 4900 0.3913 0.8845
0.6525 26.0 5096 0.3949 0.8881
0.3207 27.0 5292 0.3967 0.8881
0.4569 28.0 5488 0.3901 0.8845
0.6184 29.0 5684 0.4114 0.8917
0.6055 30.0 5880 0.4342 0.8881
0.47 31.0 6076 0.4071 0.8917
0.3507 32.0 6272 0.3838 0.8881
0.4888 33.0 6468 0.4006 0.8881
0.4276 34.0 6664 0.3909 0.8881
0.5371 35.0 6860 0.4238 0.8917
0.4826 36.0 7056 0.3843 0.8917
0.5119 37.0 7252 0.3747 0.8845
0.4192 38.0 7448 0.4232 0.8881
1.1545 39.0 7644 0.4415 0.8881
0.3206 40.0 7840 0.3937 0.8881
0.3464 41.0 8036 0.3678 0.8881
0.4016 42.0 8232 0.3849 0.8881
0.2037 43.0 8428 0.3487 0.8881
0.3795 44.0 8624 0.4298 0.8881
0.403 45.0 8820 0.3966 0.8881
0.2754 46.0 9016 0.3785 0.8845
0.5228 47.0 9212 0.4117 0.8881
0.7263 48.0 9408 0.3726 0.8845
0.8995 49.0 9604 0.4559 0.8917
0.6844 50.0 9800 0.4164 0.8881
0.2734 51.0 9996 0.3862 0.8881
0.4179 52.0 10192 0.4386 0.8917
0.3354 53.0 10388 0.3949 0.8881
0.7031 54.0 10584 0.3910 0.8881
0.586 55.0 10780 0.4216 0.8881
0.3601 56.0 10976 0.4545 0.8881
0.362 57.0 11172 0.3760 0.8845
0.6132 58.0 11368 0.4258 0.8881
0.5605 59.0 11564 0.3972 0.8881
0.5071 60.0 11760 0.3873 0.8917
0.458 61.0 11956 0.4098 0.8881
0.4401 62.0 12152 0.3859 0.8845
0.5439 63.0 12348 0.4142 0.8917
0.6099 64.0 12544 0.3970 0.8881
0.2749 65.0 12740 0.3656 0.8809
0.581 66.0 12936 0.4203 0.8881
0.6009 67.0 13132 0.4074 0.8917
0.2388 68.0 13328 0.3594 0.8845
0.6006 69.0 13524 0.4045 0.8845
0.388 70.0 13720 0.3717 0.8881
0.552 71.0 13916 0.4239 0.8881
0.3875 72.0 14112 0.3731 0.8881
0.3105 73.0 14308 0.3434 0.8845
0.4627 74.0 14504 0.3946 0.8881
0.2931 75.0 14700 0.3950 0.8845
0.4639 76.0 14896 0.3875 0.8881
0.3534 77.0 15092 0.4009 0.8881
0.3175 78.0 15288 0.4109 0.8881
0.5334 79.0 15484 0.3918 0.8881
0.4827 80.0 15680 0.3807 0.8881
0.5162 81.0 15876 0.3624 0.8845
0.4377 82.0 16072 0.3729 0.8881
0.4487 83.0 16268 0.3981 0.8917
0.5057 84.0 16464 0.3995 0.8917
0.3421 85.0 16660 0.3554 0.8881
0.4083 86.0 16856 0.3634 0.8845
0.7634 87.0 17052 0.3970 0.8881
0.2588 88.0 17248 0.4121 0.8917
0.1584 89.0 17444 0.3711 0.8881
0.2643 90.0 17640 0.3743 0.8881
0.2771 91.0 17836 0.3726 0.8881
0.336 92.0 18032 0.3758 0.8845
0.3283 93.0 18228 0.4397 0.8917
0.7224 94.0 18424 0.3869 0.8917
0.1575 95.0 18620 0.3381 0.8917
0.4062 96.0 18816 0.3684 0.8845
0.3849 97.0 19012 0.3887 0.8881
0.2755 98.0 19208 0.3725 0.8881
0.4952 99.0 19404 0.4137 0.8917
0.3807 100.0 19600 0.3923 0.8881

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.21.0