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clauculus
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image_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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emotion-classification

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  1. README.md +104 -104
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.50625
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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
@@ -32,8 +32,8 @@ 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 imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3344
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- - Accuracy: 0.5062
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
@@ -64,106 +64,106 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 10 | 2.0716 | 0.1187 |
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- | No log | 2.0 | 20 | 2.0629 | 0.1375 |
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- | No log | 3.0 | 30 | 2.0521 | 0.1562 |
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- | No log | 4.0 | 40 | 2.0437 | 0.2125 |
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- | No log | 5.0 | 50 | 2.0276 | 0.25 |
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- | No log | 6.0 | 60 | 2.0066 | 0.3063 |
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- | No log | 7.0 | 70 | 1.9779 | 0.3 |
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- | No log | 8.0 | 80 | 1.9538 | 0.3063 |
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- | No log | 9.0 | 90 | 1.9229 | 0.325 |
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- | No log | 10.0 | 100 | 1.8739 | 0.3563 |
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- | No log | 11.0 | 110 | 1.8404 | 0.3375 |
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- | No log | 12.0 | 120 | 1.7943 | 0.3688 |
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- | No log | 13.0 | 130 | 1.7616 | 0.35 |
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- | No log | 14.0 | 140 | 1.7186 | 0.3937 |
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- | No log | 15.0 | 150 | 1.6926 | 0.4062 |
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- | No log | 16.0 | 160 | 1.6778 | 0.4062 |
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- | No log | 17.0 | 170 | 1.6579 | 0.4062 |
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- | No log | 18.0 | 180 | 1.6462 | 0.4 |
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- | No log | 19.0 | 190 | 1.6143 | 0.4188 |
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- | No log | 20.0 | 200 | 1.5932 | 0.4313 |
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- | No log | 21.0 | 210 | 1.5833 | 0.4625 |
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- | No log | 22.0 | 220 | 1.5726 | 0.4437 |
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- | No log | 23.0 | 230 | 1.5545 | 0.4188 |
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- | No log | 24.0 | 240 | 1.5220 | 0.4688 |
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- | No log | 25.0 | 250 | 1.5237 | 0.4188 |
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- | No log | 26.0 | 260 | 1.5175 | 0.4375 |
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- | No log | 27.0 | 270 | 1.5008 | 0.4 |
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- | No log | 28.0 | 280 | 1.5100 | 0.4875 |
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- | No log | 29.0 | 290 | 1.4730 | 0.4938 |
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- | No log | 30.0 | 300 | 1.4842 | 0.5125 |
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- | No log | 31.0 | 310 | 1.4967 | 0.45 |
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- | No log | 32.0 | 320 | 1.4584 | 0.4562 |
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- | No log | 33.0 | 330 | 1.4458 | 0.4813 |
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- | No log | 34.0 | 340 | 1.4850 | 0.475 |
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- | No log | 35.0 | 350 | 1.4558 | 0.4688 |
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- | No log | 36.0 | 360 | 1.4438 | 0.5 |
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- | No log | 37.0 | 370 | 1.4290 | 0.475 |
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- | No log | 38.0 | 380 | 1.4347 | 0.4938 |
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- | No log | 39.0 | 390 | 1.4283 | 0.4437 |
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- | No log | 40.0 | 400 | 1.4149 | 0.4813 |
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- | No log | 41.0 | 410 | 1.3983 | 0.4813 |
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- | No log | 42.0 | 420 | 1.4079 | 0.45 |
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- | No log | 43.0 | 430 | 1.3984 | 0.45 |
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- | No log | 44.0 | 440 | 1.3866 | 0.5 |
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- | No log | 45.0 | 450 | 1.3809 | 0.4875 |
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- | No log | 46.0 | 460 | 1.3858 | 0.4813 |
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- | No log | 47.0 | 470 | 1.3981 | 0.4875 |
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- | No log | 48.0 | 480 | 1.3822 | 0.4813 |
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- | No log | 49.0 | 490 | 1.3728 | 0.4437 |
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- | 1.4038 | 50.0 | 500 | 1.3828 | 0.45 |
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- | 1.4038 | 51.0 | 510 | 1.3842 | 0.4813 |
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- | 1.4038 | 52.0 | 520 | 1.3460 | 0.4688 |
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- | 1.4038 | 53.0 | 530 | 1.3513 | 0.4938 |
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- | 1.4038 | 54.0 | 540 | 1.3645 | 0.4875 |
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- | 1.4038 | 55.0 | 550 | 1.3273 | 0.5062 |
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- | 1.4038 | 56.0 | 560 | 1.3470 | 0.525 |
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- | 1.4038 | 57.0 | 570 | 1.4006 | 0.45 |
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- | 1.4038 | 58.0 | 580 | 1.3259 | 0.5312 |
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- | 1.4038 | 59.0 | 590 | 1.3030 | 0.5062 |
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- | 1.4038 | 60.0 | 600 | 1.3526 | 0.5125 |
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- | 1.4038 | 61.0 | 610 | 1.3665 | 0.4625 |
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- | 1.4038 | 62.0 | 620 | 1.3689 | 0.4813 |
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- | 1.4038 | 63.0 | 630 | 1.3139 | 0.4813 |
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- | 1.4038 | 64.0 | 640 | 1.3618 | 0.4875 |
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- | 1.4038 | 65.0 | 650 | 1.3596 | 0.4938 |
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- | 1.4038 | 66.0 | 660 | 1.3360 | 0.4813 |
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- | 1.4038 | 67.0 | 670 | 1.3201 | 0.5062 |
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- | 1.4038 | 68.0 | 680 | 1.3615 | 0.5 |
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- | 1.4038 | 69.0 | 690 | 1.3335 | 0.5062 |
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- | 1.4038 | 70.0 | 700 | 1.2843 | 0.5687 |
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- | 1.4038 | 71.0 | 710 | 1.3697 | 0.4813 |
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- | 1.4038 | 72.0 | 720 | 1.2891 | 0.5188 |
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- | 1.4038 | 73.0 | 730 | 1.3355 | 0.5 |
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- | 1.4038 | 74.0 | 740 | 1.3400 | 0.4813 |
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- | 1.4038 | 75.0 | 750 | 1.3140 | 0.4938 |
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- | 1.4038 | 76.0 | 760 | 1.3492 | 0.4688 |
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- | 1.4038 | 77.0 | 770 | 1.2946 | 0.5188 |
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- | 1.4038 | 78.0 | 780 | 1.3635 | 0.45 |
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- | 1.4038 | 79.0 | 790 | 1.3224 | 0.5 |
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- | 1.4038 | 80.0 | 800 | 1.3092 | 0.525 |
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- | 1.4038 | 81.0 | 810 | 1.3298 | 0.475 |
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- | 1.4038 | 82.0 | 820 | 1.3626 | 0.4562 |
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- | 1.4038 | 83.0 | 830 | 1.3028 | 0.5375 |
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- | 1.4038 | 84.0 | 840 | 1.3025 | 0.5375 |
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- | 1.4038 | 85.0 | 850 | 1.3433 | 0.5188 |
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- | 1.4038 | 86.0 | 860 | 1.2508 | 0.5437 |
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- | 1.4038 | 87.0 | 870 | 1.3074 | 0.5062 |
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- | 1.4038 | 88.0 | 880 | 1.3227 | 0.4875 |
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- | 1.4038 | 89.0 | 890 | 1.3069 | 0.5188 |
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- | 1.4038 | 90.0 | 900 | 1.3278 | 0.4875 |
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- | 1.4038 | 91.0 | 910 | 1.3475 | 0.4875 |
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- | 1.4038 | 92.0 | 920 | 1.3310 | 0.4875 |
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- | 1.4038 | 93.0 | 930 | 1.3015 | 0.5062 |
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- | 1.4038 | 94.0 | 940 | 1.3635 | 0.4875 |
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- | 1.4038 | 95.0 | 950 | 1.3610 | 0.475 |
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- | 1.4038 | 96.0 | 960 | 1.2927 | 0.525 |
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- | 1.4038 | 97.0 | 970 | 1.3346 | 0.475 |
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- | 1.4038 | 98.0 | 980 | 1.3628 | 0.4625 |
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- | 1.4038 | 99.0 | 990 | 1.3301 | 0.4813 |
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- | 0.8016 | 100.0 | 1000 | 1.3301 | 0.475 |
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  ### Framework versions
 
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  metrics:
23
  - name: Accuracy
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  type: accuracy
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+ value: 0.525
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  ---
27
 
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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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  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: 1.6838
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+ - Accuracy: 0.525
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  ## Model description
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 10 | 1.3274 | 0.5125 |
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+ | No log | 2.0 | 20 | 1.3119 | 0.5188 |
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+ | No log | 3.0 | 30 | 1.3825 | 0.4625 |
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+ | No log | 4.0 | 40 | 1.2916 | 0.5312 |
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+ | No log | 5.0 | 50 | 1.2821 | 0.525 |
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+ | No log | 6.0 | 60 | 1.2407 | 0.525 |
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+ | No log | 7.0 | 70 | 1.3288 | 0.5125 |
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+ | No log | 8.0 | 80 | 1.2818 | 0.525 |
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+ | No log | 9.0 | 90 | 1.3710 | 0.4875 |
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+ | No log | 10.0 | 100 | 1.3298 | 0.5312 |
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+ | No log | 11.0 | 110 | 1.3539 | 0.475 |
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+ | No log | 12.0 | 120 | 1.4498 | 0.4688 |
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+ | No log | 13.0 | 130 | 1.5422 | 0.4437 |
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+ | No log | 14.0 | 140 | 1.4870 | 0.4625 |
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+ | No log | 15.0 | 150 | 1.4354 | 0.525 |
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+ | No log | 16.0 | 160 | 1.4286 | 0.4938 |
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+ | No log | 17.0 | 170 | 1.5332 | 0.4437 |
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+ | No log | 18.0 | 180 | 1.4164 | 0.5188 |
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+ | No log | 19.0 | 190 | 1.5024 | 0.4625 |
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+ | No log | 20.0 | 200 | 1.4730 | 0.5125 |
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+ | No log | 21.0 | 210 | 1.3083 | 0.55 |
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+ | No log | 22.0 | 220 | 1.4468 | 0.525 |
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+ | No log | 23.0 | 230 | 1.3198 | 0.525 |
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+ | No log | 24.0 | 240 | 1.3530 | 0.5563 |
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+ | No log | 25.0 | 250 | 1.4821 | 0.4938 |
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+ | No log | 26.0 | 260 | 1.3475 | 0.5437 |
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+ | No log | 27.0 | 270 | 1.5152 | 0.4875 |
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+ | No log | 28.0 | 280 | 1.4290 | 0.55 |
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+ | No log | 29.0 | 290 | 1.5505 | 0.5 |
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+ | No log | 30.0 | 300 | 1.5796 | 0.5062 |
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+ | No log | 31.0 | 310 | 1.5988 | 0.5125 |
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+ | No log | 32.0 | 320 | 1.6272 | 0.4875 |
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+ | No log | 33.0 | 330 | 1.4324 | 0.5437 |
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+ | No log | 34.0 | 340 | 1.5245 | 0.5062 |
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+ | No log | 35.0 | 350 | 1.7228 | 0.45 |
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+ | No log | 36.0 | 360 | 1.4861 | 0.525 |
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+ | No log | 37.0 | 370 | 1.5317 | 0.5312 |
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+ | No log | 38.0 | 380 | 1.7776 | 0.475 |
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+ | No log | 39.0 | 390 | 1.5386 | 0.5563 |
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+ | No log | 40.0 | 400 | 1.7608 | 0.475 |
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+ | No log | 41.0 | 410 | 1.5469 | 0.55 |
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+ | No log | 42.0 | 420 | 1.6919 | 0.4625 |
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+ | No log | 43.0 | 430 | 1.5814 | 0.525 |
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+ | No log | 44.0 | 440 | 1.5877 | 0.5125 |
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+ | No log | 45.0 | 450 | 1.6370 | 0.5188 |
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+ | No log | 46.0 | 460 | 1.7375 | 0.5188 |
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+ | No log | 47.0 | 470 | 1.7004 | 0.5 |
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+ | No log | 48.0 | 480 | 1.6309 | 0.4938 |
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+ | No log | 49.0 | 490 | 1.5931 | 0.5437 |
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+ | 0.2996 | 50.0 | 500 | 1.7687 | 0.5062 |
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+ | 0.2996 | 51.0 | 510 | 1.5321 | 0.5188 |
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+ | 0.2996 | 52.0 | 520 | 1.8099 | 0.4688 |
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+ | 0.2996 | 53.0 | 530 | 1.5138 | 0.575 |
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+ | 0.2996 | 54.0 | 540 | 1.7569 | 0.4688 |
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+ | 0.2996 | 55.0 | 550 | 1.7451 | 0.4813 |
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+ | 0.2996 | 56.0 | 560 | 1.6871 | 0.5125 |
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+ | 0.2996 | 57.0 | 570 | 1.6471 | 0.525 |
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+ | 0.2996 | 58.0 | 580 | 1.6966 | 0.525 |
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+ | 0.2996 | 59.0 | 590 | 1.7714 | 0.5 |
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+ | 0.2996 | 60.0 | 600 | 1.4985 | 0.5938 |
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+ | 0.2996 | 61.0 | 610 | 1.9804 | 0.4313 |
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+ | 0.2996 | 62.0 | 620 | 1.6116 | 0.5375 |
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+ | 0.2996 | 63.0 | 630 | 1.6056 | 0.525 |
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+ | 0.2996 | 64.0 | 640 | 1.6115 | 0.5062 |
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+ | 0.2996 | 65.0 | 650 | 1.9694 | 0.4625 |
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+ | 0.2996 | 66.0 | 660 | 1.6338 | 0.5563 |
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+ | 0.2996 | 67.0 | 670 | 1.4823 | 0.5938 |
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+ | 0.2996 | 68.0 | 680 | 1.9253 | 0.5 |
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+ | 0.2996 | 69.0 | 690 | 1.9015 | 0.4813 |
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+ | 0.2996 | 70.0 | 700 | 1.5446 | 0.5687 |
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+ | 0.2996 | 71.0 | 710 | 1.9302 | 0.4938 |
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+ | 0.2996 | 72.0 | 720 | 1.6973 | 0.5375 |
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+ | 0.2996 | 73.0 | 730 | 1.8271 | 0.5 |
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+ | 0.2996 | 74.0 | 740 | 1.7559 | 0.5188 |
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+ | 0.2996 | 75.0 | 750 | 1.8127 | 0.5312 |
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+ | 0.2996 | 76.0 | 760 | 1.8096 | 0.4938 |
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+ | 0.2996 | 77.0 | 770 | 1.8460 | 0.5062 |
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+ | 0.2996 | 78.0 | 780 | 1.8853 | 0.4813 |
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+ | 0.2996 | 79.0 | 790 | 1.7706 | 0.5125 |
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+ | 0.2996 | 80.0 | 800 | 1.8129 | 0.5312 |
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+ | 0.2996 | 81.0 | 810 | 1.9488 | 0.4688 |
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+ | 0.2996 | 82.0 | 820 | 1.8817 | 0.4813 |
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+ | 0.2996 | 83.0 | 830 | 1.6759 | 0.5563 |
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+ | 0.2996 | 84.0 | 840 | 1.6884 | 0.5 |
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+ | 0.2996 | 85.0 | 850 | 1.8146 | 0.4875 |
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+ | 0.2996 | 86.0 | 860 | 1.6610 | 0.55 |
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+ | 0.2996 | 87.0 | 870 | 1.8811 | 0.475 |
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+ | 0.2996 | 88.0 | 880 | 1.8964 | 0.5062 |
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+ | 0.2996 | 89.0 | 890 | 1.6848 | 0.5437 |
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+ | 0.2996 | 90.0 | 900 | 1.8642 | 0.4938 |
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+ | 0.2996 | 91.0 | 910 | 1.8819 | 0.5125 |
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+ | 0.2996 | 92.0 | 920 | 1.9193 | 0.4875 |
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+ | 0.2996 | 93.0 | 930 | 1.8110 | 0.5 |
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+ | 0.2996 | 94.0 | 940 | 1.9086 | 0.4813 |
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+ | 0.2996 | 95.0 | 950 | 1.8895 | 0.4625 |
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+ | 0.2996 | 96.0 | 960 | 1.7554 | 0.5312 |
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+ | 0.2996 | 97.0 | 970 | 1.8978 | 0.5188 |
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+ | 0.2996 | 98.0 | 980 | 1.9791 | 0.4875 |
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+ | 0.2996 | 99.0 | 990 | 1.7030 | 0.5687 |
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+ | 0.0883 | 100.0 | 1000 | 1.8398 | 0.4813 |
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  ### Framework versions
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
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