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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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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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model-index:
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- name: Emotion-Image-Classification-V3
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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: 0.6375
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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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# Emotion-Image-Classification-V3
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5048
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- Accuracy: 0.6375
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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: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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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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- num_epochs: 100
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 20 | 1.4141 | 0.5437 |
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| No log | 2.0 | 40 | 1.6711 | 0.4375 |
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| No log | 3.0 | 60 | 1.3988 | 0.6 |
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| No log | 4.0 | 80 | 1.5072 | 0.5625 |
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| No log | 5.0 | 100 | 1.3970 | 0.6125 |
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| No log | 6.0 | 120 | 1.3488 | 0.625 |
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| No log | 7.0 | 140 | 1.4599 | 0.5437 |
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| No log | 8.0 | 160 | 1.4678 | 0.5813 |
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| No log | 9.0 | 180 | 1.6072 | 0.5375 |
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| No log | 10.0 | 200 | 1.2243 | 0.6312 |
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| No log | 11.0 | 220 | 1.2860 | 0.5875 |
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| No log | 12.0 | 240 | 1.2472 | 0.5875 |
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| No log | 13.0 | 260 | 1.3423 | 0.5875 |
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| No log | 14.0 | 280 | 1.3879 | 0.5875 |
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| No log | 15.0 | 300 | 1.4201 | 0.575 |
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| No log | 16.0 | 320 | 1.5388 | 0.5312 |
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| No log | 17.0 | 340 | 1.5433 | 0.55 |
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| No log | 18.0 | 360 | 1.3812 | 0.5875 |
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| No log | 19.0 | 380 | 1.4629 | 0.5938 |
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| No log | 20.0 | 400 | 1.5240 | 0.525 |
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| No log | 21.0 | 420 | 1.4818 | 0.5437 |
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| No log | 22.0 | 440 | 1.4461 | 0.5687 |
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| No log | 23.0 | 460 | 1.3944 | 0.5875 |
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| No log | 24.0 | 480 | 1.6598 | 0.55 |
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| 0.1882 | 25.0 | 500 | 1.4268 | 0.6188 |
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| 0.1882 | 26.0 | 520 | 1.6246 | 0.5563 |
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| 0.1882 | 27.0 | 540 | 1.3836 | 0.6125 |
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| 0.1882 | 28.0 | 560 | 1.7652 | 0.4813 |
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| 0.1882 | 29.0 | 580 | 1.4360 | 0.5625 |
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| 0.1882 | 30.0 | 600 | 1.5103 | 0.55 |
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| 0.1882 | 31.0 | 620 | 1.4546 | 0.5563 |
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| 0.1882 | 32.0 | 640 | 1.4085 | 0.575 |
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| 0.1882 | 33.0 | 660 | 1.4729 | 0.6062 |
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| 0.1882 | 34.0 | 680 | 1.7415 | 0.5375 |
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| 0.1882 | 35.0 | 700 | 1.7349 | 0.5375 |
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| 0.1882 | 36.0 | 720 | 1.6331 | 0.5687 |
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| 0.1882 | 37.0 | 740 | 1.5159 | 0.6062 |
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| 0.1882 | 38.0 | 760 | 1.5464 | 0.5875 |
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| 0.1882 | 39.0 | 780 | 1.5402 | 0.5938 |
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| 0.1882 | 40.0 | 800 | 1.5403 | 0.6 |
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| 0.1882 | 41.0 | 820 | 1.4509 | 0.65 |
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| 0.1882 | 42.0 | 840 | 1.7641 | 0.5437 |
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| 0.1882 | 43.0 | 860 | 1.5503 | 0.5813 |
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| 0.1882 | 44.0 | 880 | 1.6178 | 0.5687 |
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| 0.1882 | 45.0 | 900 | 1.5877 | 0.6062 |
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| 0.1882 | 46.0 | 920 | 1.7210 | 0.55 |
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| 0.1882 | 47.0 | 940 | 1.5960 | 0.6188 |
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| 0.1882 | 48.0 | 960 | 1.7922 | 0.55 |
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| 0.1882 | 49.0 | 980 | 2.0035 | 0.525 |
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| 0.1299 | 50.0 | 1000 | 1.8269 | 0.5062 |
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| 0.1299 | 51.0 | 1020 | 1.6933 | 0.5687 |
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| 0.1299 | 52.0 | 1040 | 1.7252 | 0.5312 |
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| 0.1299 | 53.0 | 1060 | 1.6312 | 0.6 |
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| 0.1299 | 54.0 | 1080 | 1.8208 | 0.5375 |
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| 0.1299 | 55.0 | 1100 | 1.7589 | 0.575 |
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| 0.1299 | 56.0 | 1120 | 1.7185 | 0.5875 |
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| 0.1299 | 57.0 | 1140 | 1.7227 | 0.5437 |
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| 0.1299 | 58.0 | 1160 | 1.8849 | 0.5188 |
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| 0.1299 | 59.0 | 1180 | 1.7565 | 0.5687 |
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| 0.1299 | 60.0 | 1200 | 1.6048 | 0.6062 |
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| 0.1299 | 61.0 | 1220 | 1.5088 | 0.6125 |
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| 0.1299 | 62.0 | 1240 | 1.6270 | 0.5687 |
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| 0.1299 | 63.0 | 1260 | 1.5913 | 0.625 |
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| 0.1299 | 64.0 | 1280 | 1.7789 | 0.5625 |
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| 0.1299 | 65.0 | 1300 | 1.7923 | 0.55 |
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| 0.1299 | 66.0 | 1320 | 1.9365 | 0.575 |
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| 0.1299 | 67.0 | 1340 | 1.7365 | 0.5938 |
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| 0.1299 | 68.0 | 1360 | 1.8584 | 0.55 |
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| 0.1299 | 69.0 | 1380 | 1.9811 | 0.5062 |
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| 0.1299 | 70.0 | 1400 | 1.9433 | 0.55 |
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| 0.1299 | 71.0 | 1420 | 1.7644 | 0.575 |
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| 0.1299 | 72.0 | 1440 | 1.7661 | 0.6 |
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| 0.1299 | 73.0 | 1460 | 1.8884 | 0.5687 |
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| 0.1299 | 74.0 | 1480 | 1.7504 | 0.5813 |
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| 0.0774 | 75.0 | 1500 | 1.9648 | 0.5687 |
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| 0.0774 | 76.0 | 1520 | 1.8968 | 0.5437 |
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| 0.0774 | 77.0 | 1540 | 1.7752 | 0.5875 |
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| 0.0774 | 78.0 | 1560 | 1.7504 | 0.625 |
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| 0.0774 | 79.0 | 1580 | 1.7458 | 0.6 |
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| 0.0774 | 80.0 | 1600 | 1.8044 | 0.5938 |
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| 0.0774 | 81.0 | 1620 | 1.6748 | 0.5813 |
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| 0.0774 | 82.0 | 1640 | 1.7661 | 0.575 |
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| 0.0774 | 83.0 | 1660 | 1.8534 | 0.575 |
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| 0.0774 | 84.0 | 1680 | 1.7733 | 0.6125 |
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| 0.0774 | 85.0 | 1700 | 1.7857 | 0.575 |
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| 0.0774 | 86.0 | 1720 | 1.7397 | 0.6 |
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| 0.0774 | 87.0 | 1740 | 1.7496 | 0.5813 |
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| 0.0774 | 88.0 | 1760 | 1.8774 | 0.5813 |
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| 0.0774 | 89.0 | 1780 | 1.6830 | 0.5938 |
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| 0.0774 | 90.0 | 1800 | 1.9231 | 0.5563 |
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| 0.0774 | 91.0 | 1820 | 1.8051 | 0.5875 |
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| 0.0774 | 92.0 | 1840 | 1.8424 | 0.5938 |
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| 0.0774 | 93.0 | 1860 | 1.8644 | 0.575 |
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| 0.0774 | 94.0 | 1880 | 1.8415 | 0.5687 |
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| 0.0774 | 95.0 | 1900 | 1.8917 | 0.55 |
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| 0.0774 | 96.0 | 1920 | 1.8964 | 0.5625 |
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| 0.0774 | 97.0 | 1940 | 1.6416 | 0.5875 |
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| 0.0774 | 98.0 | 1960 | 1.7067 | 0.625 |
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| 0.0774 | 99.0 | 1980 | 1.7533 | 0.5938 |
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| 0.0569 | 100.0 | 2000 | 1.8181 | 0.5563 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.3.0
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- Datasets 2.15.0
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- Tokenizers 0.15.1
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