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microsoft-resnet-50-cartoon-emotion-detection

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: microsoft-resnet-50-cartoon-emotion-detection
    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: 0.8165137614678899
          - name: Precision
            type: precision
            value: 0.8181998512273742
          - name: Recall
            type: recall
            value: 0.8165137614678899
          - name: F1
            type: f1
            value: 0.8172526992448356

microsoft-resnet-50-cartoon-emotion-detection

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

  • Loss: 0.4801
  • Accuracy: 0.8165
  • Precision: 0.8182
  • Recall: 0.8165
  • F1: 0.8173

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: 0.00012
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 80

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
No log 0.97 8 1.3855 0.2294 0.2697 0.2294 0.2165
1.4222 1.97 16 1.3792 0.2569 0.2808 0.2569 0.2543
1.4183 2.97 24 1.3646 0.3853 0.4102 0.3853 0.3511
1.4097 3.97 32 1.3563 0.4128 0.5062 0.4128 0.3245
1.3944 4.97 40 1.3462 0.4037 0.3927 0.4037 0.2939
1.3944 5.97 48 1.3223 0.4037 0.5152 0.4037 0.2841
1.411 6.97 56 1.3040 0.4128 0.4404 0.4128 0.2985
1.346 7.97 64 1.2700 0.4954 0.4960 0.4954 0.4093
1.3031 8.97 72 1.2150 0.5596 0.5440 0.5596 0.4672
1.2371 9.97 80 1.1580 0.5963 0.5659 0.5963 0.5101
1.2371 10.97 88 1.0670 0.6055 0.7279 0.6055 0.5211
1.1736 11.97 96 0.9856 0.6606 0.5537 0.6606 0.5772
1.0457 12.97 104 0.8963 0.6697 0.7631 0.6697 0.5965
0.953 13.97 112 0.8547 0.6697 0.6885 0.6697 0.6081
0.8579 14.97 120 0.7849 0.7156 0.7396 0.7156 0.6643
0.8579 15.97 128 0.7564 0.7431 0.7372 0.7431 0.7119
0.8167 16.97 136 0.7133 0.7615 0.7507 0.7615 0.7211
0.7273 17.97 144 0.6888 0.7523 0.7379 0.7523 0.7202
0.6547 18.97 152 0.6592 0.7798 0.7773 0.7798 0.7577
0.5963 19.97 160 0.6136 0.7706 0.7642 0.7706 0.7551
0.5963 20.97 168 0.5723 0.7890 0.7802 0.7890 0.7787
0.551 21.97 176 0.5686 0.7890 0.7761 0.7890 0.7781
0.4929 22.97 184 0.5597 0.7706 0.7649 0.7706 0.7651
0.4309 23.97 192 0.5234 0.7890 0.7774 0.7890 0.7810
0.3945 24.97 200 0.5008 0.7890 0.7837 0.7890 0.7813
0.3945 25.97 208 0.5289 0.7523 0.7537 0.7523 0.7529
0.3704 26.97 216 0.4399 0.7982 0.7958 0.7982 0.7963
0.3267 27.97 224 0.4539 0.8073 0.7983 0.8073 0.8005
0.2966 28.97 232 0.4735 0.7798 0.7892 0.7798 0.7837
0.2645 29.97 240 0.4594 0.7706 0.7706 0.7706 0.7706
0.2645 30.97 248 0.4699 0.7523 0.7554 0.7523 0.7533
0.2527 31.97 256 0.4551 0.7890 0.7856 0.7890 0.7857
0.2202 32.97 264 0.4458 0.8165 0.8198 0.8165 0.8170
0.2006 33.97 272 0.4632 0.7798 0.7941 0.7798 0.7850
0.1589 34.97 280 0.4651 0.7890 0.7993 0.7890 0.7925
0.1589 35.97 288 0.4595 0.7798 0.7824 0.7798 0.7804
0.153 36.97 296 0.4584 0.7615 0.7691 0.7615 0.7633
0.1427 37.97 304 0.4608 0.7798 0.7830 0.7798 0.7796
0.113 38.97 312 0.4571 0.7890 0.7922 0.7890 0.7899
0.1146 39.97 320 0.5270 0.7615 0.7651 0.7615 0.7613
0.1146 40.97 328 0.4888 0.7706 0.7782 0.7706 0.7710
0.1275 41.97 336 0.4523 0.7890 0.7809 0.7890 0.7837
0.0959 42.97 344 0.4697 0.7798 0.7753 0.7798 0.7767
0.0882 43.97 352 0.4286 0.7706 0.7686 0.7706 0.7686
0.0847 44.97 360 0.5317 0.7890 0.7993 0.7890 0.7925
0.0847 45.97 368 0.5431 0.7615 0.7700 0.7615 0.7647
0.0813 46.97 376 0.4432 0.8257 0.8435 0.8257 0.8284
0.0768 47.97 384 0.4886 0.7982 0.8005 0.7982 0.7956
0.0627 48.97 392 0.5373 0.7982 0.8072 0.7982 0.8010
0.0688 49.97 400 0.5897 0.7798 0.7892 0.7798 0.7822
0.0688 50.97 408 0.5115 0.7982 0.8015 0.7982 0.7992
0.0676 51.97 416 0.4881 0.7982 0.7998 0.7982 0.7978
0.0539 52.97 424 0.4820 0.8073 0.8139 0.8073 0.8077
0.0596 53.97 432 0.4450 0.8257 0.8246 0.8257 0.8244
0.0611 54.97 440 0.5057 0.7890 0.8008 0.7890 0.7924
0.0611 55.97 448 0.4918 0.7982 0.8056 0.7982 0.8008
0.0643 56.97 456 0.5946 0.7523 0.7587 0.7523 0.7545
0.0605 57.97 464 0.4888 0.8073 0.8239 0.8073 0.8121
0.063 58.97 472 0.5917 0.7890 0.8051 0.7890 0.7937
0.0595 59.97 480 0.5117 0.7890 0.7904 0.7890 0.7894
0.0595 60.97 488 0.5497 0.7615 0.7692 0.7615 0.7635
0.0554 61.97 496 0.4742 0.8165 0.8101 0.8165 0.8126
0.0557 62.97 504 0.5369 0.7890 0.7886 0.7890 0.7886
0.0539 63.97 512 0.5440 0.7890 0.7922 0.7890 0.7899
0.048 64.97 520 0.5924 0.7890 0.7878 0.7890 0.7883
0.048 65.97 528 0.4863 0.8440 0.8440 0.8440 0.8440
0.045 66.97 536 0.5850 0.8073 0.8076 0.8073 0.8047
0.047 67.97 544 0.4939 0.8257 0.8212 0.8257 0.8227
0.0412 68.97 552 0.4850 0.7890 0.7912 0.7890 0.7900
0.0392 69.97 560 0.5066 0.8257 0.8265 0.8257 0.8258
0.0392 70.97 568 0.4965 0.8073 0.8053 0.8073 0.8058
0.0423 71.97 576 0.4717 0.8349 0.8376 0.8349 0.8351
0.0471 72.97 584 0.4845 0.8257 0.8378 0.8257 0.8296
0.0322 73.97 592 0.5188 0.7706 0.7689 0.7706 0.7693
0.042 74.97 600 0.5242 0.7706 0.7699 0.7706 0.7701
0.042 75.97 608 0.5945 0.7798 0.7824 0.7798 0.7804
0.0416 76.97 616 0.5432 0.7982 0.8038 0.7982 0.7993
0.0399 77.97 624 0.5381 0.7982 0.8072 0.7982 0.7994
0.0439 78.97 632 0.6181 0.7798 0.7878 0.7798 0.7827
0.0462 79.97 640 0.4801 0.8165 0.8182 0.8165 0.8173

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.11.0