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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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- precision |
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- f1 |
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model-index: |
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- name: emotion_classification |
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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: 0.64375 |
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- name: Precision |
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type: precision |
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value: 0.650616883116883 |
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- name: F1 |
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type: f1 |
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value: 0.6344950707077283 |
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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_classification |
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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: 1.1553 |
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- Accuracy: 0.6438 |
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- Precision: 0.6506 |
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- F1: 0.6345 |
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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: 3e-05 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 300 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| |
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| 2.0799 | 1.0 | 10 | 2.0707 | 0.1313 | 0.1740 | 0.1156 | |
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| 2.0811 | 2.0 | 20 | 2.0681 | 0.1437 | 0.1617 | 0.1245 | |
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| 2.0709 | 3.0 | 30 | 2.0640 | 0.1562 | 0.1544 | 0.1330 | |
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| 2.0701 | 4.0 | 40 | 2.0590 | 0.1688 | 0.1463 | 0.1431 | |
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| 2.0639 | 5.0 | 50 | 2.0529 | 0.1812 | 0.1676 | 0.1613 | |
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| 2.0499 | 6.0 | 60 | 2.0439 | 0.2 | 0.2050 | 0.1871 | |
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| 2.0387 | 7.0 | 70 | 2.0322 | 0.25 | 0.2679 | 0.2373 | |
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| 2.0235 | 8.0 | 80 | 2.0141 | 0.3312 | 0.3638 | 0.3331 | |
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| 1.9933 | 9.0 | 90 | 1.9883 | 0.3375 | 0.3752 | 0.3392 | |
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| 1.9573 | 10.0 | 100 | 1.9473 | 0.3563 | 0.3940 | 0.3535 | |
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| 1.912 | 11.0 | 110 | 1.8863 | 0.3875 | 0.4352 | 0.3759 | |
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| 1.8306 | 12.0 | 120 | 1.8102 | 0.3875 | 0.4062 | 0.3586 | |
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| 1.7479 | 13.0 | 130 | 1.7158 | 0.4062 | 0.4056 | 0.3689 | |
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| 1.665 | 14.0 | 140 | 1.6250 | 0.475 | 0.4543 | 0.4248 | |
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| 1.6115 | 15.0 | 150 | 1.5597 | 0.4875 | 0.4646 | 0.4414 | |
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| 1.5716 | 16.0 | 160 | 1.5112 | 0.5125 | 0.4846 | 0.4575 | |
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| 1.5062 | 17.0 | 170 | 1.4672 | 0.525 | 0.4932 | 0.4925 | |
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| 1.4655 | 18.0 | 180 | 1.4262 | 0.5312 | 0.5018 | 0.4876 | |
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| 1.413 | 19.0 | 190 | 1.3851 | 0.575 | 0.5253 | 0.5317 | |
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| 1.3758 | 20.0 | 200 | 1.3421 | 0.5625 | 0.5900 | 0.5113 | |
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| 1.317 | 21.0 | 210 | 1.3156 | 0.55 | 0.5835 | 0.4996 | |
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| 1.291 | 22.0 | 220 | 1.2712 | 0.5938 | 0.6374 | 0.5601 | |
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| 1.2369 | 23.0 | 230 | 1.2697 | 0.5563 | 0.5681 | 0.5250 | |
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| 1.2139 | 24.0 | 240 | 1.2439 | 0.5625 | 0.5733 | 0.5417 | |
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| 1.1766 | 25.0 | 250 | 1.2228 | 0.5938 | 0.6099 | 0.5735 | |
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| 1.1483 | 26.0 | 260 | 1.2464 | 0.5625 | 0.6016 | 0.5508 | |
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| 1.1344 | 27.0 | 270 | 1.1877 | 0.5875 | 0.6142 | 0.5718 | |
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| 1.0898 | 28.0 | 280 | 1.1871 | 0.6 | 0.6481 | 0.5817 | |
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| 1.0515 | 29.0 | 290 | 1.1553 | 0.6438 | 0.6506 | 0.6345 | |
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| 1.0628 | 30.0 | 300 | 1.1603 | 0.575 | 0.6209 | 0.5727 | |
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| 1.0257 | 31.0 | 310 | 1.1326 | 0.6125 | 0.6312 | 0.6109 | |
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| 1.0048 | 32.0 | 320 | 1.1450 | 0.6125 | 0.6402 | 0.6079 | |
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| 0.9646 | 33.0 | 330 | 1.1250 | 0.6062 | 0.6161 | 0.6004 | |
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| 0.9231 | 34.0 | 340 | 1.1299 | 0.6 | 0.6183 | 0.5976 | |
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| 0.8944 | 35.0 | 350 | 1.1312 | 0.5938 | 0.5996 | 0.5885 | |
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| 0.9001 | 36.0 | 360 | 1.1293 | 0.625 | 0.6358 | 0.6220 | |
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| 0.8587 | 37.0 | 370 | 1.1415 | 0.6062 | 0.6122 | 0.6037 | |
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| 0.8708 | 38.0 | 380 | 1.1171 | 0.6062 | 0.6379 | 0.5985 | |
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| 0.843 | 39.0 | 390 | 1.1220 | 0.625 | 0.6658 | 0.6229 | |
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| 0.8038 | 40.0 | 400 | 1.1144 | 0.6188 | 0.6243 | 0.6153 | |
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| 0.7815 | 41.0 | 410 | 1.1538 | 0.575 | 0.6042 | 0.5679 | |
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| 0.7289 | 42.0 | 420 | 1.1125 | 0.6062 | 0.6218 | 0.6024 | |
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| 0.7255 | 43.0 | 430 | 1.1401 | 0.6 | 0.6307 | 0.5947 | |
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| 0.7182 | 44.0 | 440 | 1.1092 | 0.6 | 0.6121 | 0.5916 | |
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| 0.6533 | 45.0 | 450 | 1.1219 | 0.625 | 0.6448 | 0.6268 | |
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| 0.6658 | 46.0 | 460 | 1.1322 | 0.6125 | 0.6272 | 0.6135 | |
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| 0.6293 | 47.0 | 470 | 1.1306 | 0.6 | 0.6075 | 0.5980 | |
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| 0.6287 | 48.0 | 480 | 1.1227 | 0.6125 | 0.6210 | 0.6099 | |
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| 0.622 | 49.0 | 490 | 1.1441 | 0.5938 | 0.6154 | 0.5940 | |
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| 0.6004 | 50.0 | 500 | 1.1119 | 0.625 | 0.6267 | 0.6206 | |
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| 0.606 | 51.0 | 510 | 1.1301 | 0.5938 | 0.6146 | 0.5925 | |
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| 0.5924 | 52.0 | 520 | 1.1552 | 0.6062 | 0.6135 | 0.6022 | |
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| 0.5639 | 53.0 | 530 | 1.1956 | 0.5938 | 0.6411 | 0.5945 | |
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| 0.5434 | 54.0 | 540 | 1.1843 | 0.5813 | 0.5925 | 0.5765 | |
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| 0.5479 | 55.0 | 550 | 1.1529 | 0.6125 | 0.6247 | 0.6142 | |
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| 0.5227 | 56.0 | 560 | 1.1730 | 0.5687 | 0.5724 | 0.5628 | |
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| 0.5402 | 57.0 | 570 | 1.1919 | 0.6 | 0.6075 | 0.5954 | |
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| 0.4971 | 58.0 | 580 | 1.1761 | 0.5938 | 0.5984 | 0.5925 | |
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| 0.5004 | 59.0 | 590 | 1.2305 | 0.5687 | 0.5957 | 0.5645 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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