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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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End of training

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  1. README.md +68 -64
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.64375
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  - name: Precision
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  type: precision
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- value: 0.6639732142857142
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  - name: F1
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  type: f1
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- value: 0.640682001352849
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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: 1.0750
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- - Accuracy: 0.6438
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- - Precision: 0.6640
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- - F1: 0.6407
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  ## Model description
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@@ -68,68 +68,72 @@ The following hyperparameters were used during training:
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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: 50
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- - num_epochs: 200
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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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- | 2.0755 | 1.0 | 10 | 2.0787 | 0.1437 | 0.1529 | 0.1414 |
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- | 2.0711 | 2.0 | 20 | 2.0698 | 0.1875 | 0.1926 | 0.1832 |
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- | 2.0533 | 3.0 | 30 | 2.0520 | 0.2 | 0.2127 | 0.1961 |
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- | 2.0225 | 4.0 | 40 | 2.0173 | 0.225 | 0.2228 | 0.2054 |
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- | 1.9569 | 5.0 | 50 | 1.9289 | 0.2812 | 0.3345 | 0.2544 |
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- | 1.8501 | 6.0 | 60 | 1.7792 | 0.3688 | 0.4904 | 0.3225 |
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- | 1.7072 | 7.0 | 70 | 1.6236 | 0.4313 | 0.4131 | 0.3883 |
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- | 1.6065 | 8.0 | 80 | 1.5276 | 0.45 | 0.4533 | 0.3920 |
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- | 1.539 | 9.0 | 90 | 1.4747 | 0.4938 | 0.4748 | 0.4563 |
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- | 1.5086 | 10.0 | 100 | 1.4393 | 0.4938 | 0.4557 | 0.4466 |
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- | 1.4479 | 11.0 | 110 | 1.3893 | 0.5188 | 0.4563 | 0.4696 |
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- | 1.3683 | 12.0 | 120 | 1.3534 | 0.5437 | 0.5081 | 0.5149 |
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- | 1.3288 | 13.0 | 130 | 1.3392 | 0.5563 | 0.5569 | 0.5323 |
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- | 1.2514 | 14.0 | 140 | 1.2723 | 0.5625 | 0.5467 | 0.5246 |
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- | 1.2116 | 15.0 | 150 | 1.2526 | 0.5875 | 0.5554 | 0.5601 |
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- | 1.1824 | 16.0 | 160 | 1.2047 | 0.5938 | 0.6100 | 0.5697 |
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- | 1.1323 | 17.0 | 170 | 1.1950 | 0.5813 | 0.5331 | 0.5472 |
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- | 1.0782 | 18.0 | 180 | 1.1802 | 0.5875 | 0.5911 | 0.5807 |
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- | 1.0304 | 19.0 | 190 | 1.1534 | 0.6125 | 0.6133 | 0.6012 |
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- | 0.982 | 20.0 | 200 | 1.1302 | 0.6 | 0.5923 | 0.5806 |
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- | 0.9309 | 21.0 | 210 | 1.1849 | 0.5938 | 0.6157 | 0.5723 |
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- | 0.9205 | 22.0 | 220 | 1.1483 | 0.6 | 0.6137 | 0.5882 |
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- | 0.8275 | 23.0 | 230 | 1.1332 | 0.5938 | 0.6192 | 0.5894 |
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- | 0.8472 | 24.0 | 240 | 1.1195 | 0.625 | 0.6444 | 0.6242 |
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- | 0.7974 | 25.0 | 250 | 1.1444 | 0.6062 | 0.6277 | 0.6035 |
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- | 0.7532 | 26.0 | 260 | 1.1312 | 0.5875 | 0.6036 | 0.5832 |
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- | 0.7596 | 27.0 | 270 | 1.1217 | 0.6062 | 0.6412 | 0.6098 |
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- | 0.6818 | 28.0 | 280 | 1.1736 | 0.5625 | 0.6180 | 0.5473 |
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- | 0.6484 | 29.0 | 290 | 1.1630 | 0.5563 | 0.5887 | 0.5367 |
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- | 0.6578 | 30.0 | 300 | 1.0750 | 0.6438 | 0.6640 | 0.6407 |
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- | 0.6235 | 31.0 | 310 | 1.0676 | 0.6438 | 0.6556 | 0.6422 |
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- | 0.5966 | 32.0 | 320 | 1.0531 | 0.6438 | 0.6421 | 0.6385 |
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- | 0.5819 | 33.0 | 330 | 1.1244 | 0.6188 | 0.6315 | 0.6176 |
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- | 0.5585 | 34.0 | 340 | 1.1466 | 0.5813 | 0.6136 | 0.5790 |
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- | 0.5696 | 35.0 | 350 | 1.0703 | 0.6438 | 0.6614 | 0.6481 |
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- | 0.5476 | 36.0 | 360 | 1.1136 | 0.6438 | 0.6764 | 0.6466 |
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- | 0.475 | 37.0 | 370 | 1.1122 | 0.6375 | 0.6612 | 0.6340 |
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- | 0.5381 | 38.0 | 380 | 1.1547 | 0.6188 | 0.6570 | 0.6122 |
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- | 0.5161 | 39.0 | 390 | 1.2268 | 0.5875 | 0.6161 | 0.5704 |
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- | 0.4528 | 40.0 | 400 | 1.1065 | 0.6188 | 0.6314 | 0.6122 |
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- | 0.401 | 41.0 | 410 | 1.1209 | 0.6438 | 0.6550 | 0.6440 |
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- | 0.4067 | 42.0 | 420 | 1.1440 | 0.6312 | 0.6345 | 0.6251 |
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- | 0.3831 | 43.0 | 430 | 1.1972 | 0.6188 | 0.6480 | 0.6075 |
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- | 0.4073 | 44.0 | 440 | 1.2422 | 0.6062 | 0.6644 | 0.6028 |
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- | 0.371 | 45.0 | 450 | 1.2152 | 0.5875 | 0.6087 | 0.5848 |
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- | 0.396 | 46.0 | 460 | 1.1972 | 0.6125 | 0.6306 | 0.6106 |
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- | 0.3322 | 47.0 | 470 | 1.2979 | 0.5813 | 0.6158 | 0.5811 |
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- | 0.3691 | 48.0 | 480 | 1.1657 | 0.625 | 0.6371 | 0.6162 |
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- | 0.3219 | 49.0 | 490 | 1.1786 | 0.6 | 0.6417 | 0.5997 |
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- | 0.3371 | 50.0 | 500 | 1.2126 | 0.6188 | 0.6396 | 0.6149 |
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- | 0.3781 | 51.0 | 510 | 1.2246 | 0.6 | 0.6244 | 0.5972 |
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- | 0.3629 | 52.0 | 520 | 1.1820 | 0.6188 | 0.6437 | 0.6122 |
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- | 0.3025 | 53.0 | 530 | 1.1795 | 0.6062 | 0.6326 | 0.6063 |
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- | 0.309 | 54.0 | 540 | 1.1647 | 0.625 | 0.6510 | 0.6252 |
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- | 0.2999 | 55.0 | 550 | 1.2023 | 0.6375 | 0.6449 | 0.6373 |
 
 
 
 
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  ### Framework versions
 
24
  metrics:
25
  - 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
 
40
 
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.
42
  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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  - 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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