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finetuned-FER2013

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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: finetuned-FER2013
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7081156391501219

finetuned-FER2013

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8366
  • Accuracy: 0.7081

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-06
  • 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
1.8119 1.0 202 1.7993 0.3079
1.6155 2.0 404 1.5446 0.4302
1.4279 3.0 606 1.3084 0.5301
1.3222 4.0 808 1.1817 0.5590
1.2532 5.0 1010 1.1026 0.5789
1.2019 6.0 1212 1.0432 0.5998
1.2037 7.0 1414 1.0030 0.6137
1.1757 8.0 1616 0.9873 0.6235
1.1359 9.0 1818 0.9377 0.6423
1.1282 10.0 2020 0.9231 0.6486
1.1019 11.0 2222 0.9011 0.6562
1.0494 12.0 2424 0.8968 0.6545
0.9951 13.0 2626 0.8876 0.6607
1.0121 14.0 2828 0.8720 0.6695
1.0571 15.0 3030 0.8776 0.6691
1.0049 16.0 3232 0.8627 0.6733
0.988 17.0 3434 0.8639 0.6719
0.9955 18.0 3636 0.8397 0.6806
0.9381 19.0 3838 0.8430 0.6820
0.9911 20.0 4040 0.8370 0.6837
0.9305 21.0 4242 0.8373 0.6837
0.9653 22.0 4444 0.8283 0.6883
0.9134 23.0 4646 0.8289 0.6879
0.9098 24.0 4848 0.8365 0.6837
0.8761 25.0 5050 0.8190 0.6869
0.9067 26.0 5252 0.8303 0.6876
0.8765 27.0 5454 0.8188 0.6942
0.8486 28.0 5656 0.8142 0.6959
0.9357 29.0 5858 0.8114 0.6984
0.9037 30.0 6060 0.8150 0.6917
0.8758 31.0 6262 0.8165 0.6931
0.8688 32.0 6464 0.8061 0.6994
0.8736 33.0 6666 0.8056 0.6994
0.8785 34.0 6868 0.8045 0.6991
0.8292 35.0 7070 0.8095 0.6987
0.8407 36.0 7272 0.8096 0.6956
0.8609 37.0 7474 0.8137 0.6984
0.9055 38.0 7676 0.8054 0.7018
0.8355 39.0 7878 0.8080 0.6980
0.8391 40.0 8080 0.8087 0.6966
0.7987 41.0 8282 0.8041 0.6998
0.818 42.0 8484 0.8070 0.7039
0.7836 43.0 8686 0.8091 0.7025
0.8348 44.0 8888 0.8047 0.7025
0.8205 45.0 9090 0.8076 0.7025
0.8023 46.0 9292 0.8056 0.7053
0.8241 47.0 9494 0.8022 0.7039
0.763 48.0 9696 0.8079 0.6994
0.7422 49.0 9898 0.8062 0.7039
0.7762 50.0 10100 0.8090 0.6998
0.7786 51.0 10302 0.8122 0.6994
0.8027 52.0 10504 0.8129 0.7043
0.7966 53.0 10706 0.8094 0.7039
0.8103 54.0 10908 0.8107 0.7039
0.7827 55.0 11110 0.8126 0.7057
0.7949 56.0 11312 0.8104 0.7119
0.7511 57.0 11514 0.8122 0.7050
0.7727 58.0 11716 0.8123 0.7078
0.7723 59.0 11918 0.8194 0.7015
0.7796 60.0 12120 0.8193 0.7053
0.7768 61.0 12322 0.8159 0.7029
0.7604 62.0 12524 0.8081 0.7085
0.7784 63.0 12726 0.8169 0.7106
0.7235 64.0 12928 0.8131 0.7015
0.7384 65.0 13130 0.8149 0.7085
0.6638 66.0 13332 0.8192 0.7078
0.6998 67.0 13534 0.8243 0.7113
0.7249 68.0 13736 0.8200 0.7015
0.6809 69.0 13938 0.8140 0.7081
0.701 70.0 14140 0.8177 0.7095
0.7122 71.0 14342 0.8245 0.7053
0.7269 72.0 14544 0.8245 0.7050
0.6973 73.0 14746 0.8207 0.7095
0.7241 74.0 14948 0.8210 0.7057
0.7397 75.0 15150 0.8230 0.7060
0.6832 76.0 15352 0.8308 0.7057
0.7213 77.0 15554 0.8256 0.7025
0.7115 78.0 15756 0.8291 0.7057
0.688 79.0 15958 0.8337 0.7088
0.6997 80.0 16160 0.8312 0.7060
0.6924 81.0 16362 0.8321 0.7053
0.7382 82.0 16564 0.8340 0.7050
0.7513 83.0 16766 0.8320 0.7015
0.656 84.0 16968 0.8389 0.7053
0.6503 85.0 17170 0.8321 0.7085
0.6661 86.0 17372 0.8355 0.7092
0.7026 87.0 17574 0.8339 0.7088
0.76 88.0 17776 0.8361 0.7092
0.696 89.0 17978 0.8343 0.7106
0.6713 90.0 18180 0.8337 0.7106
0.6621 91.0 18382 0.8349 0.7057
0.7042 92.0 18584 0.8360 0.7085
0.7087 93.0 18786 0.8353 0.7085
0.64 94.0 18988 0.8371 0.7088
0.659 95.0 19190 0.8376 0.7071
0.6246 96.0 19392 0.8376 0.7088
0.6797 97.0 19594 0.8368 0.7092
0.6652 98.0 19796 0.8376 0.7092
0.629 99.0 19998 0.8370 0.7088
0.6762 100.0 20200 0.8366 0.7081

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0