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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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- image_folder |
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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: image_folder |
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type: image_folder |
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config: FastJobs--Visual_Emotional_Analysis |
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split: train |
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args: FastJobs--Visual_Emotional_Analysis |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.65625 |
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- name: Precision |
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type: precision |
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value: 0.6864745278875714 |
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- name: F1 |
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type: f1 |
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value: 0.6531282051282051 |
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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 image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0743 |
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- Accuracy: 0.6562 |
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- Precision: 0.6865 |
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- F1: 0.6531 |
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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: 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: 100 |
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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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| 2.0912 | 1.0 | 10 | 2.0884 | 0.0938 | 0.0557 | 0.0679 | |
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| 2.086 | 2.0 | 20 | 2.0835 | 0.1062 | 0.1076 | 0.0825 | |
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| 2.0724 | 3.0 | 30 | 2.0743 | 0.15 | 0.1595 | 0.1235 | |
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| 2.0575 | 4.0 | 40 | 2.0614 | 0.1625 | 0.1451 | 0.1291 | |
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| 2.0375 | 5.0 | 50 | 2.0399 | 0.2125 | 0.2375 | 0.1880 | |
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| 1.9952 | 6.0 | 60 | 1.9954 | 0.2875 | 0.4219 | 0.2692 | |
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| 1.9309 | 7.0 | 70 | 1.9096 | 0.3312 | 0.4116 | 0.3141 | |
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| 1.8219 | 8.0 | 80 | 1.7690 | 0.375 | 0.4091 | 0.3375 | |
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| 1.6907 | 9.0 | 90 | 1.6323 | 0.4 | 0.4548 | 0.3595 | |
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| 1.5937 | 10.0 | 100 | 1.5317 | 0.4437 | 0.4015 | 0.4174 | |
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| 1.5157 | 11.0 | 110 | 1.4620 | 0.5312 | 0.4945 | 0.5078 | |
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| 1.4458 | 12.0 | 120 | 1.4050 | 0.5125 | 0.4734 | 0.4880 | |
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| 1.3712 | 13.0 | 130 | 1.3719 | 0.5375 | 0.5776 | 0.5236 | |
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| 1.3043 | 14.0 | 140 | 1.3033 | 0.5687 | 0.6482 | 0.5547 | |
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| 1.2424 | 15.0 | 150 | 1.2497 | 0.5813 | 0.5970 | 0.5619 | |
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| 1.2369 | 16.0 | 160 | 1.2423 | 0.5375 | 0.4994 | 0.5061 | |
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| 1.1596 | 17.0 | 170 | 1.2109 | 0.5563 | 0.5086 | 0.5216 | |
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| 1.1252 | 18.0 | 180 | 1.1889 | 0.5813 | 0.5772 | 0.5622 | |
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| 1.0746 | 19.0 | 190 | 1.1752 | 0.5625 | 0.5843 | 0.5631 | |
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| 1.0496 | 20.0 | 200 | 1.1402 | 0.6062 | 0.5995 | 0.5911 | |
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| 0.9874 | 21.0 | 210 | 1.1470 | 0.5875 | 0.5897 | 0.5720 | |
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| 0.9423 | 22.0 | 220 | 1.1294 | 0.6188 | 0.6174 | 0.6072 | |
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| 0.8842 | 23.0 | 230 | 1.1335 | 0.6 | 0.6216 | 0.6004 | |
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| 0.8817 | 24.0 | 240 | 1.1002 | 0.6 | 0.6078 | 0.5970 | |
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| 0.8365 | 25.0 | 250 | 1.1237 | 0.625 | 0.6392 | 0.6209 | |
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| 0.7965 | 26.0 | 260 | 1.1781 | 0.55 | 0.5888 | 0.5419 | |
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| 0.7829 | 27.0 | 270 | 1.1278 | 0.6 | 0.6219 | 0.5947 | |
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| 0.7269 | 28.0 | 280 | 1.1144 | 0.6 | 0.6386 | 0.5937 | |
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| 0.7158 | 29.0 | 290 | 1.1245 | 0.6125 | 0.6524 | 0.5939 | |
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| 0.7178 | 30.0 | 300 | 1.0692 | 0.6188 | 0.6344 | 0.6159 | |
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| 0.6704 | 31.0 | 310 | 1.0568 | 0.65 | 0.6724 | 0.6514 | |
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| 0.6371 | 32.0 | 320 | 1.0411 | 0.65 | 0.6529 | 0.6465 | |
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| 0.6317 | 33.0 | 330 | 1.1018 | 0.6438 | 0.6732 | 0.6416 | |
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| 0.5625 | 34.0 | 340 | 1.0743 | 0.6562 | 0.6865 | 0.6531 | |
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| 0.5717 | 35.0 | 350 | 1.1658 | 0.6062 | 0.6636 | 0.6094 | |
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| 0.5807 | 36.0 | 360 | 1.1473 | 0.625 | 0.6654 | 0.6161 | |
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| 0.5269 | 37.0 | 370 | 1.1367 | 0.6188 | 0.6317 | 0.6150 | |
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| 0.5284 | 38.0 | 380 | 1.0724 | 0.6438 | 0.6625 | 0.6449 | |
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| 0.5715 | 39.0 | 390 | 1.1805 | 0.575 | 0.6076 | 0.5711 | |
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| 0.486 | 40.0 | 400 | 1.1676 | 0.5938 | 0.6379 | 0.5892 | |
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| 0.4581 | 41.0 | 410 | 1.1633 | 0.6312 | 0.6583 | 0.6298 | |
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| 0.4364 | 42.0 | 420 | 1.1371 | 0.6312 | 0.6353 | 0.6255 | |
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| 0.4117 | 43.0 | 430 | 1.2004 | 0.625 | 0.6748 | 0.6086 | |
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| 0.4433 | 44.0 | 440 | 1.1082 | 0.625 | 0.6322 | 0.6232 | |
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| 0.4031 | 45.0 | 450 | 1.2251 | 0.5875 | 0.6395 | 0.5944 | |
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| 0.4205 | 46.0 | 460 | 1.2513 | 0.5938 | 0.6196 | 0.5934 | |
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| 0.3524 | 47.0 | 470 | 1.1704 | 0.6125 | 0.6303 | 0.6147 | |
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| 0.4094 | 48.0 | 480 | 1.1930 | 0.5875 | 0.6071 | 0.5892 | |
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| 0.369 | 49.0 | 490 | 1.1970 | 0.6188 | 0.6509 | 0.6171 | |
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| 0.3666 | 50.0 | 500 | 1.2280 | 0.6 | 0.6171 | 0.5971 | |
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| 0.4054 | 51.0 | 510 | 1.2725 | 0.5625 | 0.5807 | 0.5599 | |
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| 0.4247 | 52.0 | 520 | 1.2380 | 0.6188 | 0.6385 | 0.6128 | |
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| 0.3791 | 53.0 | 530 | 1.2402 | 0.5813 | 0.6153 | 0.5806 | |
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| 0.3241 | 54.0 | 540 | 1.2491 | 0.5687 | 0.5817 | 0.5676 | |
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| 0.3268 | 55.0 | 550 | 1.2575 | 0.5938 | 0.6058 | 0.5956 | |
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| 0.3419 | 56.0 | 560 | 1.3199 | 0.6 | 0.6160 | 0.5930 | |
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| 0.3657 | 57.0 | 570 | 1.2408 | 0.6188 | 0.6441 | 0.6207 | |
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| 0.3327 | 58.0 | 580 | 1.2430 | 0.6125 | 0.6200 | 0.6107 | |
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| 0.3126 | 59.0 | 590 | 1.3995 | 0.5312 | 0.5619 | 0.5158 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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