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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: 0.66875 |
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- name: Precision |
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type: precision |
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value: 0.684222027972028 |
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- name: F1 |
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type: f1 |
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value: 0.6649370603045093 |
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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: 1.0254 |
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- Accuracy: 0.6687 |
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- Precision: 0.6842 |
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- F1: 0.6649 |
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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: 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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| 2.079 | 1.0 | 10 | 2.0759 | 0.1437 | 0.1305 | 0.1297 | |
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| 2.0798 | 2.0 | 20 | 2.0725 | 0.1688 | 0.1495 | 0.1503 | |
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| 2.0758 | 3.0 | 30 | 2.0668 | 0.2 | 0.1992 | 0.1859 | |
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| 2.0602 | 4.0 | 40 | 2.0595 | 0.225 | 0.2219 | 0.2100 | |
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| 2.0456 | 5.0 | 50 | 2.0495 | 0.225 | 0.2285 | 0.2105 | |
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| 2.0303 | 6.0 | 60 | 2.0324 | 0.2437 | 0.2546 | 0.2267 | |
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| 2.0009 | 7.0 | 70 | 1.9983 | 0.2437 | 0.2661 | 0.2291 | |
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| 1.9482 | 8.0 | 80 | 1.9342 | 0.3375 | 0.3320 | 0.3183 | |
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| 1.8709 | 9.0 | 90 | 1.8475 | 0.4 | 0.3524 | 0.3583 | |
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| 1.7828 | 10.0 | 100 | 1.7259 | 0.4562 | 0.4074 | 0.4111 | |
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| 1.6841 | 11.0 | 110 | 1.6324 | 0.4688 | 0.4211 | 0.4182 | |
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| 1.6047 | 12.0 | 120 | 1.5508 | 0.4375 | 0.4049 | 0.3908 | |
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| 1.5343 | 13.0 | 130 | 1.4942 | 0.5188 | 0.5115 | 0.4980 | |
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| 1.4606 | 14.0 | 140 | 1.4133 | 0.55 | 0.5063 | 0.5083 | |
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| 1.3935 | 15.0 | 150 | 1.3513 | 0.5312 | 0.5377 | 0.5050 | |
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| 1.3695 | 16.0 | 160 | 1.2981 | 0.6062 | 0.6190 | 0.5899 | |
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| 1.2956 | 17.0 | 170 | 1.2630 | 0.5687 | 0.5654 | 0.5479 | |
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| 1.2481 | 18.0 | 180 | 1.2470 | 0.5875 | 0.5931 | 0.5735 | |
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| 1.2084 | 19.0 | 190 | 1.2095 | 0.5938 | 0.6143 | 0.5899 | |
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| 1.1676 | 20.0 | 200 | 1.1918 | 0.5938 | 0.6006 | 0.5788 | |
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| 1.0999 | 21.0 | 210 | 1.2066 | 0.5875 | 0.6020 | 0.5690 | |
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| 1.071 | 22.0 | 220 | 1.1474 | 0.6 | 0.5997 | 0.5852 | |
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| 0.9925 | 23.0 | 230 | 1.1266 | 0.6312 | 0.6504 | 0.6283 | |
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| 0.961 | 24.0 | 240 | 1.1031 | 0.5938 | 0.6021 | 0.5901 | |
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| 0.9364 | 25.0 | 250 | 1.1458 | 0.6 | 0.6199 | 0.5907 | |
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| 0.8906 | 26.0 | 260 | 1.1339 | 0.5875 | 0.6158 | 0.5789 | |
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| 0.882 | 27.0 | 270 | 1.0824 | 0.6312 | 0.6543 | 0.6303 | |
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| 0.827 | 28.0 | 280 | 1.1464 | 0.5875 | 0.6521 | 0.5793 | |
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| 0.7791 | 29.0 | 290 | 1.1309 | 0.575 | 0.5998 | 0.5566 | |
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| 0.7621 | 30.0 | 300 | 1.0579 | 0.6125 | 0.6277 | 0.6068 | |
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| 0.7245 | 31.0 | 310 | 1.0418 | 0.6562 | 0.6633 | 0.6533 | |
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| 0.6868 | 32.0 | 320 | 1.0555 | 0.6375 | 0.6470 | 0.6329 | |
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| 0.653 | 33.0 | 330 | 1.1451 | 0.5938 | 0.6330 | 0.5944 | |
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| 0.6102 | 34.0 | 340 | 1.0254 | 0.6687 | 0.6842 | 0.6649 | |
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| 0.5977 | 35.0 | 350 | 1.0981 | 0.625 | 0.6482 | 0.6227 | |
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| 0.6258 | 36.0 | 360 | 1.0975 | 0.6438 | 0.6773 | 0.6346 | |
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| 0.5444 | 37.0 | 370 | 1.1195 | 0.6125 | 0.6408 | 0.6147 | |
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| 0.5558 | 38.0 | 380 | 1.0637 | 0.625 | 0.6323 | 0.6201 | |
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| 0.5716 | 39.0 | 390 | 1.1407 | 0.6062 | 0.6463 | 0.6111 | |
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| 0.5048 | 40.0 | 400 | 1.1153 | 0.6312 | 0.6407 | 0.6244 | |
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| 0.4646 | 41.0 | 410 | 1.1072 | 0.625 | 0.6284 | 0.6225 | |
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| 0.463 | 42.0 | 420 | 1.1086 | 0.6062 | 0.6062 | 0.6026 | |
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| 0.4321 | 43.0 | 430 | 1.1725 | 0.6 | 0.6304 | 0.5960 | |
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| 0.49 | 44.0 | 440 | 1.1325 | 0.6188 | 0.6423 | 0.6166 | |
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| 0.408 | 45.0 | 450 | 1.2134 | 0.575 | 0.5865 | 0.5721 | |
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| 0.4296 | 46.0 | 460 | 1.2182 | 0.6188 | 0.6492 | 0.6175 | |
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| 0.3328 | 47.0 | 470 | 1.1789 | 0.6188 | 0.6378 | 0.6205 | |
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| 0.3781 | 48.0 | 480 | 1.2054 | 0.6125 | 0.6158 | 0.6077 | |
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| 0.3326 | 49.0 | 490 | 1.2308 | 0.5938 | 0.6148 | 0.5941 | |
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| 0.3526 | 50.0 | 500 | 1.2640 | 0.6 | 0.6038 | 0.5959 | |
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| 0.3967 | 51.0 | 510 | 1.3154 | 0.5437 | 0.5635 | 0.5410 | |
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| 0.4286 | 52.0 | 520 | 1.2358 | 0.6188 | 0.6488 | 0.6140 | |
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| 0.3411 | 53.0 | 530 | 1.1959 | 0.625 | 0.6368 | 0.6192 | |
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| 0.3455 | 54.0 | 540 | 1.2526 | 0.6 | 0.6168 | 0.5973 | |
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| 0.3224 | 55.0 | 550 | 1.1988 | 0.625 | 0.6490 | 0.6208 | |
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| 0.3015 | 56.0 | 560 | 1.2067 | 0.6062 | 0.6030 | 0.6005 | |
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| 0.322 | 57.0 | 570 | 1.2124 | 0.6188 | 0.6279 | 0.6181 | |
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| 0.2991 | 58.0 | 580 | 1.2274 | 0.6312 | 0.6368 | 0.6294 | |
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| 0.3199 | 59.0 | 590 | 1.2649 | 0.5938 | 0.5876 | 0.5880 | |
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| 0.3204 | 60.0 | 600 | 1.2636 | 0.6062 | 0.6239 | 0.6002 | |
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| 0.2831 | 61.0 | 610 | 1.3039 | 0.5875 | 0.5974 | 0.5832 | |
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| 0.2723 | 62.0 | 620 | 1.2620 | 0.625 | 0.6558 | 0.6236 | |
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| 0.2806 | 63.0 | 630 | 1.2368 | 0.6312 | 0.6364 | 0.6294 | |
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| 0.2621 | 64.0 | 640 | 1.2783 | 0.6062 | 0.6160 | 0.6049 | |
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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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