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Raihan004
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Action_model

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  1. README.md +34 -34
  2. model.safetensors +1 -1
  3. runs/Apr17_18-44-31_49f673bfb0ca/events.out.tfevents.1713379472.49f673bfb0ca.34.0 +3 -0
  4. training_args.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: 0.7666666666666667
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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: 1.0234
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- - Accuracy: 0.7667
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  ## Model description
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@@ -65,37 +65,37 @@ 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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- | 0.4299 | 0.32 | 100 | 0.7981 | 0.7457 |
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- | 0.3903 | 0.64 | 200 | 0.7173 | 0.7771 |
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- | 0.4296 | 0.96 | 300 | 0.6869 | 0.7876 |
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- | 0.3589 | 1.27 | 400 | 0.9108 | 0.7314 |
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- | 0.3007 | 1.59 | 500 | 0.9720 | 0.7133 |
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- | 0.2817 | 1.91 | 600 | 0.8504 | 0.7486 |
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- | 0.2754 | 2.23 | 700 | 0.9009 | 0.7410 |
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- | 0.2226 | 2.55 | 800 | 0.9020 | 0.7495 |
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- | 0.285 | 2.87 | 900 | 1.0012 | 0.7295 |
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- | 0.2307 | 3.18 | 1000 | 0.8204 | 0.7810 |
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- | 0.2398 | 3.5 | 1100 | 0.8857 | 0.7695 |
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- | 0.1948 | 3.82 | 1200 | 0.9110 | 0.7571 |
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- | 0.1962 | 4.14 | 1300 | 0.9775 | 0.7533 |
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- | 0.2159 | 4.46 | 1400 | 0.9719 | 0.7457 |
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- | 0.1361 | 4.78 | 1500 | 0.9262 | 0.7571 |
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- | 0.1898 | 5.1 | 1600 | 0.9130 | 0.7705 |
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- | 0.1153 | 5.41 | 1700 | 1.0409 | 0.7438 |
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- | 0.1489 | 5.73 | 1800 | 1.0176 | 0.7495 |
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- | 0.1515 | 6.05 | 1900 | 1.0507 | 0.7486 |
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- | 0.1126 | 6.37 | 2000 | 1.1423 | 0.7210 |
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- | 0.1319 | 6.69 | 2100 | 1.1008 | 0.7467 |
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- | 0.1424 | 7.01 | 2200 | 1.0798 | 0.7419 |
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- | 0.0955 | 7.32 | 2300 | 1.0767 | 0.7505 |
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- | 0.1077 | 7.64 | 2400 | 1.0920 | 0.7457 |
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- | 0.1048 | 7.96 | 2500 | 1.0040 | 0.7733 |
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- | 0.0965 | 8.28 | 2600 | 1.0384 | 0.7610 |
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- | 0.0995 | 8.6 | 2700 | 1.0423 | 0.7648 |
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- | 0.1213 | 8.92 | 2800 | 1.0544 | 0.7619 |
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- | 0.0863 | 9.24 | 2900 | 1.0454 | 0.7629 |
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- | 0.0926 | 9.55 | 3000 | 1.0380 | 0.7676 |
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- | 0.0536 | 9.87 | 3100 | 1.0234 | 0.7667 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7876190476190477
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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: 0.9250
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+ - Accuracy: 0.7876
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.1285 | 0.32 | 100 | 1.0131 | 0.7743 |
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+ | 0.7868 | 0.64 | 200 | 0.7684 | 0.7867 |
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+ | 0.6015 | 0.96 | 300 | 0.7090 | 0.7714 |
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+ | 0.5209 | 1.27 | 400 | 0.7650 | 0.7571 |
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+ | 0.4536 | 1.59 | 500 | 0.7826 | 0.7419 |
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+ | 0.4069 | 1.91 | 600 | 0.6878 | 0.7876 |
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+ | 0.3244 | 2.23 | 700 | 0.9184 | 0.7238 |
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+ | 0.2618 | 2.55 | 800 | 0.8178 | 0.7552 |
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+ | 0.342 | 2.87 | 900 | 0.8192 | 0.7648 |
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+ | 0.2778 | 3.18 | 1000 | 0.7542 | 0.7848 |
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+ | 0.2331 | 3.5 | 1100 | 0.8133 | 0.7695 |
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+ | 0.2426 | 3.82 | 1200 | 0.9022 | 0.7476 |
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+ | 0.2363 | 4.14 | 1300 | 0.9009 | 0.7619 |
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+ | 0.2143 | 4.46 | 1400 | 0.8545 | 0.7790 |
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+ | 0.1624 | 4.78 | 1500 | 0.9543 | 0.7533 |
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+ | 0.2302 | 5.1 | 1600 | 0.8138 | 0.78 |
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+ | 0.1682 | 5.41 | 1700 | 0.8490 | 0.7790 |
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+ | 0.1674 | 5.73 | 1800 | 0.9097 | 0.7724 |
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+ | 0.1595 | 6.05 | 1900 | 1.0542 | 0.7486 |
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+ | 0.1335 | 6.37 | 2000 | 0.8957 | 0.7876 |
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+ | 0.1696 | 6.69 | 2100 | 0.8860 | 0.7781 |
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+ | 0.148 | 7.01 | 2200 | 0.9529 | 0.7733 |
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+ | 0.1281 | 7.32 | 2300 | 0.9364 | 0.7848 |
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+ | 0.1274 | 7.64 | 2400 | 0.9252 | 0.7676 |
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+ | 0.1585 | 7.96 | 2500 | 0.9068 | 0.7914 |
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+ | 0.0985 | 8.28 | 2600 | 0.9400 | 0.7829 |
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+ | 0.1211 | 8.6 | 2700 | 0.9464 | 0.7790 |
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+ | 0.1459 | 8.92 | 2800 | 0.9800 | 0.7695 |
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+ | 0.1221 | 9.24 | 2900 | 0.9457 | 0.78 |
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+ | 0.1072 | 9.55 | 3000 | 0.9209 | 0.7857 |
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+ | 0.0607 | 9.87 | 3100 | 0.9250 | 0.7876 |
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  ### Framework versions
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