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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: microsoft-resnet-50-cartoon-emotion-detection |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8165137614678899 |
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- name: Precision |
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type: precision |
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value: 0.8181998512273742 |
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- name: Recall |
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type: recall |
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value: 0.8165137614678899 |
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- name: F1 |
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type: f1 |
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value: 0.8172526992448356 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# microsoft-resnet-50-cartoon-emotion-detection |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4801 |
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- Accuracy: 0.8165 |
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- Precision: 0.8182 |
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- Recall: 0.8165 |
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- F1: 0.8173 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00012 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 80 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.97 | 8 | 1.3855 | 0.2294 | 0.2697 | 0.2294 | 0.2165 | |
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| 1.4222 | 1.97 | 16 | 1.3792 | 0.2569 | 0.2808 | 0.2569 | 0.2543 | |
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| 1.4183 | 2.97 | 24 | 1.3646 | 0.3853 | 0.4102 | 0.3853 | 0.3511 | |
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| 1.4097 | 3.97 | 32 | 1.3563 | 0.4128 | 0.5062 | 0.4128 | 0.3245 | |
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| 1.3944 | 4.97 | 40 | 1.3462 | 0.4037 | 0.3927 | 0.4037 | 0.2939 | |
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| 1.3944 | 5.97 | 48 | 1.3223 | 0.4037 | 0.5152 | 0.4037 | 0.2841 | |
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| 1.411 | 6.97 | 56 | 1.3040 | 0.4128 | 0.4404 | 0.4128 | 0.2985 | |
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| 1.346 | 7.97 | 64 | 1.2700 | 0.4954 | 0.4960 | 0.4954 | 0.4093 | |
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| 1.3031 | 8.97 | 72 | 1.2150 | 0.5596 | 0.5440 | 0.5596 | 0.4672 | |
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| 1.2371 | 9.97 | 80 | 1.1580 | 0.5963 | 0.5659 | 0.5963 | 0.5101 | |
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| 1.2371 | 10.97 | 88 | 1.0670 | 0.6055 | 0.7279 | 0.6055 | 0.5211 | |
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| 1.1736 | 11.97 | 96 | 0.9856 | 0.6606 | 0.5537 | 0.6606 | 0.5772 | |
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| 1.0457 | 12.97 | 104 | 0.8963 | 0.6697 | 0.7631 | 0.6697 | 0.5965 | |
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| 0.953 | 13.97 | 112 | 0.8547 | 0.6697 | 0.6885 | 0.6697 | 0.6081 | |
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| 0.8579 | 14.97 | 120 | 0.7849 | 0.7156 | 0.7396 | 0.7156 | 0.6643 | |
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| 0.8579 | 15.97 | 128 | 0.7564 | 0.7431 | 0.7372 | 0.7431 | 0.7119 | |
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| 0.8167 | 16.97 | 136 | 0.7133 | 0.7615 | 0.7507 | 0.7615 | 0.7211 | |
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| 0.7273 | 17.97 | 144 | 0.6888 | 0.7523 | 0.7379 | 0.7523 | 0.7202 | |
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| 0.6547 | 18.97 | 152 | 0.6592 | 0.7798 | 0.7773 | 0.7798 | 0.7577 | |
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| 0.5963 | 19.97 | 160 | 0.6136 | 0.7706 | 0.7642 | 0.7706 | 0.7551 | |
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| 0.5963 | 20.97 | 168 | 0.5723 | 0.7890 | 0.7802 | 0.7890 | 0.7787 | |
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| 0.551 | 21.97 | 176 | 0.5686 | 0.7890 | 0.7761 | 0.7890 | 0.7781 | |
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| 0.4929 | 22.97 | 184 | 0.5597 | 0.7706 | 0.7649 | 0.7706 | 0.7651 | |
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| 0.4309 | 23.97 | 192 | 0.5234 | 0.7890 | 0.7774 | 0.7890 | 0.7810 | |
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| 0.3945 | 24.97 | 200 | 0.5008 | 0.7890 | 0.7837 | 0.7890 | 0.7813 | |
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| 0.3945 | 25.97 | 208 | 0.5289 | 0.7523 | 0.7537 | 0.7523 | 0.7529 | |
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| 0.3704 | 26.97 | 216 | 0.4399 | 0.7982 | 0.7958 | 0.7982 | 0.7963 | |
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| 0.3267 | 27.97 | 224 | 0.4539 | 0.8073 | 0.7983 | 0.8073 | 0.8005 | |
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| 0.2966 | 28.97 | 232 | 0.4735 | 0.7798 | 0.7892 | 0.7798 | 0.7837 | |
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| 0.2645 | 29.97 | 240 | 0.4594 | 0.7706 | 0.7706 | 0.7706 | 0.7706 | |
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| 0.2645 | 30.97 | 248 | 0.4699 | 0.7523 | 0.7554 | 0.7523 | 0.7533 | |
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| 0.2527 | 31.97 | 256 | 0.4551 | 0.7890 | 0.7856 | 0.7890 | 0.7857 | |
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| 0.2202 | 32.97 | 264 | 0.4458 | 0.8165 | 0.8198 | 0.8165 | 0.8170 | |
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| 0.2006 | 33.97 | 272 | 0.4632 | 0.7798 | 0.7941 | 0.7798 | 0.7850 | |
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| 0.1589 | 34.97 | 280 | 0.4651 | 0.7890 | 0.7993 | 0.7890 | 0.7925 | |
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| 0.1589 | 35.97 | 288 | 0.4595 | 0.7798 | 0.7824 | 0.7798 | 0.7804 | |
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| 0.153 | 36.97 | 296 | 0.4584 | 0.7615 | 0.7691 | 0.7615 | 0.7633 | |
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| 0.1427 | 37.97 | 304 | 0.4608 | 0.7798 | 0.7830 | 0.7798 | 0.7796 | |
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| 0.113 | 38.97 | 312 | 0.4571 | 0.7890 | 0.7922 | 0.7890 | 0.7899 | |
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| 0.1146 | 39.97 | 320 | 0.5270 | 0.7615 | 0.7651 | 0.7615 | 0.7613 | |
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| 0.1146 | 40.97 | 328 | 0.4888 | 0.7706 | 0.7782 | 0.7706 | 0.7710 | |
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| 0.1275 | 41.97 | 336 | 0.4523 | 0.7890 | 0.7809 | 0.7890 | 0.7837 | |
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| 0.0959 | 42.97 | 344 | 0.4697 | 0.7798 | 0.7753 | 0.7798 | 0.7767 | |
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| 0.0882 | 43.97 | 352 | 0.4286 | 0.7706 | 0.7686 | 0.7706 | 0.7686 | |
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| 0.0847 | 44.97 | 360 | 0.5317 | 0.7890 | 0.7993 | 0.7890 | 0.7925 | |
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| 0.0847 | 45.97 | 368 | 0.5431 | 0.7615 | 0.7700 | 0.7615 | 0.7647 | |
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| 0.0813 | 46.97 | 376 | 0.4432 | 0.8257 | 0.8435 | 0.8257 | 0.8284 | |
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| 0.0768 | 47.97 | 384 | 0.4886 | 0.7982 | 0.8005 | 0.7982 | 0.7956 | |
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| 0.0627 | 48.97 | 392 | 0.5373 | 0.7982 | 0.8072 | 0.7982 | 0.8010 | |
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| 0.0688 | 49.97 | 400 | 0.5897 | 0.7798 | 0.7892 | 0.7798 | 0.7822 | |
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| 0.0688 | 50.97 | 408 | 0.5115 | 0.7982 | 0.8015 | 0.7982 | 0.7992 | |
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| 0.0676 | 51.97 | 416 | 0.4881 | 0.7982 | 0.7998 | 0.7982 | 0.7978 | |
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| 0.0539 | 52.97 | 424 | 0.4820 | 0.8073 | 0.8139 | 0.8073 | 0.8077 | |
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| 0.0596 | 53.97 | 432 | 0.4450 | 0.8257 | 0.8246 | 0.8257 | 0.8244 | |
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| 0.0611 | 54.97 | 440 | 0.5057 | 0.7890 | 0.8008 | 0.7890 | 0.7924 | |
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| 0.0611 | 55.97 | 448 | 0.4918 | 0.7982 | 0.8056 | 0.7982 | 0.8008 | |
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| 0.0643 | 56.97 | 456 | 0.5946 | 0.7523 | 0.7587 | 0.7523 | 0.7545 | |
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| 0.0605 | 57.97 | 464 | 0.4888 | 0.8073 | 0.8239 | 0.8073 | 0.8121 | |
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| 0.063 | 58.97 | 472 | 0.5917 | 0.7890 | 0.8051 | 0.7890 | 0.7937 | |
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| 0.0595 | 59.97 | 480 | 0.5117 | 0.7890 | 0.7904 | 0.7890 | 0.7894 | |
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| 0.0595 | 60.97 | 488 | 0.5497 | 0.7615 | 0.7692 | 0.7615 | 0.7635 | |
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| 0.0554 | 61.97 | 496 | 0.4742 | 0.8165 | 0.8101 | 0.8165 | 0.8126 | |
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| 0.0557 | 62.97 | 504 | 0.5369 | 0.7890 | 0.7886 | 0.7890 | 0.7886 | |
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| 0.0539 | 63.97 | 512 | 0.5440 | 0.7890 | 0.7922 | 0.7890 | 0.7899 | |
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| 0.048 | 64.97 | 520 | 0.5924 | 0.7890 | 0.7878 | 0.7890 | 0.7883 | |
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| 0.048 | 65.97 | 528 | 0.4863 | 0.8440 | 0.8440 | 0.8440 | 0.8440 | |
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| 0.045 | 66.97 | 536 | 0.5850 | 0.8073 | 0.8076 | 0.8073 | 0.8047 | |
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| 0.047 | 67.97 | 544 | 0.4939 | 0.8257 | 0.8212 | 0.8257 | 0.8227 | |
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| 0.0412 | 68.97 | 552 | 0.4850 | 0.7890 | 0.7912 | 0.7890 | 0.7900 | |
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| 0.0392 | 69.97 | 560 | 0.5066 | 0.8257 | 0.8265 | 0.8257 | 0.8258 | |
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| 0.0392 | 70.97 | 568 | 0.4965 | 0.8073 | 0.8053 | 0.8073 | 0.8058 | |
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| 0.0423 | 71.97 | 576 | 0.4717 | 0.8349 | 0.8376 | 0.8349 | 0.8351 | |
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| 0.0471 | 72.97 | 584 | 0.4845 | 0.8257 | 0.8378 | 0.8257 | 0.8296 | |
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| 0.0322 | 73.97 | 592 | 0.5188 | 0.7706 | 0.7689 | 0.7706 | 0.7693 | |
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| 0.042 | 74.97 | 600 | 0.5242 | 0.7706 | 0.7699 | 0.7706 | 0.7701 | |
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| 0.042 | 75.97 | 608 | 0.5945 | 0.7798 | 0.7824 | 0.7798 | 0.7804 | |
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| 0.0416 | 76.97 | 616 | 0.5432 | 0.7982 | 0.8038 | 0.7982 | 0.7993 | |
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| 0.0399 | 77.97 | 624 | 0.5381 | 0.7982 | 0.8072 | 0.7982 | 0.7994 | |
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| 0.0439 | 78.97 | 632 | 0.6181 | 0.7798 | 0.7878 | 0.7798 | 0.7827 | |
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| 0.0462 | 79.97 | 640 | 0.4801 | 0.8165 | 0.8182 | 0.8165 | 0.8173 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.11.0 |
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