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Action_model_ViT_384

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Action_model_ViT_384

This model is a fine-tuned version of google/vit-base-patch16-384 on the action_class dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4520
  • Accuracy: 0.8612

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.946 0.19 100 0.7540 0.7803
0.9248 0.37 200 0.6282 0.7961
0.7968 0.56 300 0.5834 0.8102
0.6992 0.75 400 0.5647 0.8330
0.7331 0.93 500 0.5430 0.8295
0.5822 1.12 600 0.5894 0.8172
0.5906 1.31 700 0.6862 0.7909
0.5911 1.49 800 0.5369 0.8313
0.4564 1.68 900 0.4657 0.8576
0.6416 1.87 1000 0.5697 0.8190
0.5653 2.05 1100 0.6152 0.8102
0.4145 2.24 1200 0.5793 0.8225
0.4743 2.43 1300 0.4642 0.8576
0.4908 2.61 1400 0.4520 0.8612
0.523 2.8 1500 0.4989 0.8453
0.3315 2.99 1600 0.4786 0.8576
0.2779 3.17 1700 0.5546 0.8524
0.2984 3.36 1800 0.4977 0.8576
0.5914 3.54 1900 0.6296 0.8225
0.3236 3.73 2000 0.7225 0.8172
0.6194 3.92 2100 0.5783 0.8506
0.5066 4.1 2200 0.5825 0.8260
0.3532 4.29 2300 0.5606 0.8594
0.3531 4.48 2400 0.5068 0.8699
0.2573 4.66 2500 0.5632 0.8576
0.2713 4.85 2600 0.5047 0.8612
0.3538 5.04 2700 0.5988 0.8471
0.2291 5.22 2800 0.5751 0.8453
0.2976 5.41 2900 0.5781 0.8559
0.296 5.6 3000 0.5499 0.8664
0.3776 5.78 3100 0.5718 0.8612
0.2213 5.97 3200 0.5421 0.8682
0.325 6.16 3300 0.6453 0.8453
0.1594 6.34 3400 0.5558 0.8647
0.3377 6.53 3500 0.6619 0.8418
0.3743 6.72 3600 0.5446 0.8717
0.2327 6.9 3700 0.5484 0.8735
0.1659 7.09 3800 0.6629 0.8471
0.4036 7.28 3900 0.6510 0.8330
0.2084 7.46 4000 0.5640 0.8629
0.2251 7.65 4100 0.6379 0.8541
0.192 7.84 4200 0.5897 0.8629
0.1956 8.02 4300 0.5874 0.8699
0.1446 8.21 4400 0.6462 0.8594
0.2971 8.4 4500 0.5909 0.8735
0.2665 8.58 4600 0.6769 0.8612
0.2937 8.77 4700 0.6760 0.8506
0.1437 8.96 4800 0.6566 0.8489
0.1433 9.14 4900 0.6659 0.8418
0.2069 9.33 5000 0.6825 0.8541
0.2095 9.51 5100 0.6157 0.8664
0.1579 9.7 5200 0.6693 0.8629
0.1962 9.89 5300 0.6911 0.8524
0.3149 10.07 5400 0.6260 0.8559
0.2166 10.26 5500 0.6200 0.8770
0.1259 10.45 5600 0.7164 0.8576
0.1892 10.63 5700 0.7182 0.8612
0.1953 10.82 5800 0.7193 0.8418
0.2392 11.01 5900 0.6621 0.8664
0.1594 11.19 6000 0.7471 0.8489
0.2156 11.38 6100 0.7316 0.8612
0.137 11.57 6200 0.6837 0.8699
0.181 11.75 6300 0.6595 0.8647
0.2049 11.94 6400 0.6982 0.8506
0.1028 12.13 6500 0.6771 0.8682
0.1347 12.31 6600 0.6841 0.8699
0.1269 12.5 6700 0.7226 0.8594
0.2288 12.69 6800 0.7083 0.8629
0.1094 12.87 6900 0.7455 0.8471
0.0661 13.06 7000 0.7330 0.8541
0.1811 13.25 7100 0.7363 0.8436
0.2225 13.43 7200 0.7757 0.8453
0.1619 13.62 7300 0.7361 0.8576
0.2032 13.81 7400 0.7656 0.8576
0.0216 13.99 7500 0.7760 0.8629
0.2476 14.18 7600 0.7723 0.8612
0.1616 14.37 7700 0.7247 0.8787
0.1142 14.55 7800 0.7907 0.8699
0.0906 14.74 7900 0.7829 0.8647
0.2199 14.93 8000 0.7427 0.8717
0.0643 15.11 8100 0.7280 0.8699
0.1685 15.3 8200 0.8381 0.8541
0.1677 15.49 8300 0.8638 0.8506
0.1399 15.67 8400 0.8423 0.8612
0.1041 15.86 8500 0.8051 0.8541
0.2223 16.04 8600 0.7768 0.8647
0.1016 16.23 8700 0.7965 0.8647
0.065 16.42 8800 0.8331 0.8418
0.1156 16.6 8900 0.8023 0.8629
0.2263 16.79 9000 0.8116 0.8594
0.1197 16.98 9100 0.8490 0.8576
0.1931 17.16 9200 0.8194 0.8612
0.1289 17.35 9300 0.8353 0.8489
0.2039 17.54 9400 0.8163 0.8453
0.0825 17.72 9500 0.7942 0.8524
0.0712 17.91 9600 0.8027 0.8559
0.244 18.1 9700 0.7803 0.8664
0.1482 18.28 9800 0.7754 0.8629
0.1829 18.47 9900 0.7810 0.8594
0.019 18.66 10000 0.7972 0.8559
0.061 18.84 10100 0.8180 0.8576
0.117 19.03 10200 0.8319 0.8559
0.1858 19.22 10300 0.8432 0.8559
0.1087 19.4 10400 0.8273 0.8594
0.1983 19.59 10500 0.8257 0.8612
0.2453 19.78 10600 0.8177 0.8576
0.1189 19.96 10700 0.8201 0.8594

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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Evaluation results

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