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dennisjooo
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emotion_classification

Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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  1. README.md +65 -69
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -27,10 +27,10 @@ model-index:
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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
@@ -40,10 +40,10 @@ should probably proofread and complete it, then remove this comment. -->
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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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@@ -75,70 +75,66 @@ The following hyperparameters were used during training:
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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
 
27
  value: 0.66875
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  - name: Precision
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  type: precision
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+ value: 0.7104119480438352
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  - name: F1
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  type: f1
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+ value: 0.6712765732314218
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  ---
35
 
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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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41
  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: 1.0511
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  - Accuracy: 0.6687
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+ - Precision: 0.7104
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+ - F1: 0.6713
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  ## Model description
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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 | 2.0895 | 0.0563 | 0.0604 | 0.0521 |
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+ | 2.0789 | 2.0 | 20 | 2.0851 | 0.0563 | 0.0602 | 0.0529 |
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+ | 2.0717 | 3.0 | 30 | 2.0773 | 0.0813 | 0.0858 | 0.0783 |
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+ | 2.0613 | 4.0 | 40 | 2.0658 | 0.125 | 0.1997 | 0.1333 |
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+ | 2.0445 | 5.0 | 50 | 2.0483 | 0.1875 | 0.2569 | 0.1934 |
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+ | 2.0176 | 6.0 | 60 | 2.0206 | 0.2313 | 0.2692 | 0.2384 |
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+ | 1.9894 | 7.0 | 70 | 1.9763 | 0.3063 | 0.3033 | 0.2983 |
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+ | 1.9232 | 8.0 | 80 | 1.8912 | 0.3625 | 0.3307 | 0.3194 |
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+ | 1.8256 | 9.0 | 90 | 1.7775 | 0.4062 | 0.3531 | 0.3600 |
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+ | 1.732 | 10.0 | 100 | 1.6580 | 0.4688 | 0.4158 | 0.4133 |
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+ | 1.6406 | 11.0 | 110 | 1.5597 | 0.5 | 0.4358 | 0.4370 |
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+ | 1.5584 | 12.0 | 120 | 1.4855 | 0.5125 | 0.4792 | 0.4784 |
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+ | 1.4898 | 13.0 | 130 | 1.4248 | 0.5437 | 0.5011 | 0.5098 |
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+ | 1.4216 | 14.0 | 140 | 1.3692 | 0.5687 | 0.5255 | 0.5289 |
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+ | 1.3701 | 15.0 | 150 | 1.3158 | 0.5687 | 0.5346 | 0.5360 |
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+ | 1.3438 | 16.0 | 160 | 1.2842 | 0.5437 | 0.5451 | 0.5098 |
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+ | 1.2799 | 17.0 | 170 | 1.2620 | 0.5625 | 0.5169 | 0.5194 |
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+ | 1.2481 | 18.0 | 180 | 1.2321 | 0.5938 | 0.6003 | 0.5811 |
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+ | 1.1993 | 19.0 | 190 | 1.2108 | 0.5687 | 0.5640 | 0.5412 |
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+ | 1.1599 | 20.0 | 200 | 1.1853 | 0.55 | 0.5434 | 0.5259 |
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+ | 1.1087 | 21.0 | 210 | 1.1839 | 0.5563 | 0.5670 | 0.5380 |
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+ | 1.0757 | 22.0 | 220 | 1.1905 | 0.55 | 0.5682 | 0.5308 |
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+ | 0.9985 | 23.0 | 230 | 1.1509 | 0.6375 | 0.6714 | 0.6287 |
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+ | 0.9776 | 24.0 | 240 | 1.1048 | 0.6188 | 0.6222 | 0.6127 |
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+ | 0.9331 | 25.0 | 250 | 1.1196 | 0.6125 | 0.6345 | 0.6072 |
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+ | 0.8887 | 26.0 | 260 | 1.1424 | 0.5938 | 0.6174 | 0.5867 |
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+ | 0.879 | 27.0 | 270 | 1.1232 | 0.6062 | 0.6342 | 0.5978 |
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+ | 0.8369 | 28.0 | 280 | 1.1172 | 0.6 | 0.6480 | 0.5865 |
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+ | 0.7864 | 29.0 | 290 | 1.1285 | 0.5938 | 0.6819 | 0.5763 |
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+ | 0.7775 | 30.0 | 300 | 1.0511 | 0.6687 | 0.7104 | 0.6713 |
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+ | 0.7281 | 31.0 | 310 | 1.0295 | 0.6562 | 0.6596 | 0.6514 |
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+ | 0.7348 | 32.0 | 320 | 1.0398 | 0.6375 | 0.6353 | 0.6319 |
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+ | 0.6896 | 33.0 | 330 | 1.0729 | 0.6062 | 0.6205 | 0.6062 |
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+ | 0.613 | 34.0 | 340 | 1.0505 | 0.6438 | 0.6595 | 0.6421 |
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+ | 0.6034 | 35.0 | 350 | 1.0827 | 0.6375 | 0.6593 | 0.6376 |
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+ | 0.6236 | 36.0 | 360 | 1.1271 | 0.6125 | 0.6238 | 0.6087 |
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+ | 0.5607 | 37.0 | 370 | 1.0985 | 0.6062 | 0.6254 | 0.6015 |
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+ | 0.5835 | 38.0 | 380 | 1.0791 | 0.6375 | 0.6624 | 0.6370 |
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+ | 0.5889 | 39.0 | 390 | 1.1300 | 0.6062 | 0.6529 | 0.6092 |
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+ | 0.5137 | 40.0 | 400 | 1.1062 | 0.625 | 0.6457 | 0.6226 |
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+ | 0.4804 | 41.0 | 410 | 1.1452 | 0.6188 | 0.6403 | 0.6158 |
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+ | 0.4811 | 42.0 | 420 | 1.1271 | 0.6375 | 0.6478 | 0.6347 |
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+ | 0.5179 | 43.0 | 430 | 1.1942 | 0.5875 | 0.6185 | 0.5874 |
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+ | 0.4744 | 44.0 | 440 | 1.1515 | 0.6125 | 0.6329 | 0.6160 |
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+ | 0.4327 | 45.0 | 450 | 1.1321 | 0.6375 | 0.6669 | 0.6412 |
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+ | 0.4565 | 46.0 | 460 | 1.1742 | 0.625 | 0.6478 | 0.6251 |
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+ | 0.4006 | 47.0 | 470 | 1.1675 | 0.6062 | 0.6361 | 0.6079 |
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+ | 0.4541 | 48.0 | 480 | 1.1542 | 0.6125 | 0.6404 | 0.6152 |
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+ | 0.3689 | 49.0 | 490 | 1.2190 | 0.5875 | 0.6134 | 0.5896 |
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+ | 0.3794 | 50.0 | 500 | 1.2002 | 0.6062 | 0.6155 | 0.6005 |
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+ | 0.429 | 51.0 | 510 | 1.2904 | 0.575 | 0.6207 | 0.5849 |
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+ | 0.431 | 52.0 | 520 | 1.2416 | 0.5875 | 0.6028 | 0.5794 |
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+ | 0.3813 | 53.0 | 530 | 1.2073 | 0.6125 | 0.6449 | 0.6142 |
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+ | 0.365 | 54.0 | 540 | 1.2083 | 0.6062 | 0.6454 | 0.6075 |
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+ | 0.3714 | 55.0 | 550 | 1.1627 | 0.6375 | 0.6576 | 0.6390 |
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+ | 0.3393 | 56.0 | 560 | 1.1620 | 0.6438 | 0.6505 | 0.6389 |
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+ | 0.3676 | 57.0 | 570 | 1.1501 | 0.625 | 0.6294 | 0.6258 |
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+ | 0.3371 | 58.0 | 580 | 1.2779 | 0.5875 | 0.6000 | 0.5792 |
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+ | 0.3325 | 59.0 | 590 | 1.2719 | 0.575 | 0.5843 | 0.5651 |
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+ | 0.3509 | 60.0 | 600 | 1.2956 | 0.6 | 0.6422 | 0.6059 |
 
 
 
 
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  ### Framework versions
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