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DazMashaly
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VIT_large_ieee

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Model card Files
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  1. README.md +45 -29
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -2,7 +2,6 @@
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -16,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-beans-demo-v5
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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 bact dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0498
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- - Accuracy: 0.9882
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  ## Model description
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@@ -38,41 +37,58 @@ 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: 0.0002
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  - train_batch_size: 16
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  - eval_batch_size: 8
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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: linear
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- - num_epochs: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.1764 | 0.17 | 100 | 0.2424 | 0.9345 |
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- | 0.2401 | 0.34 | 200 | 0.2909 | 0.9160 |
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- | 0.1986 | 0.5 | 300 | 0.2258 | 0.9358 |
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- | 0.1579 | 0.67 | 400 | 0.2699 | 0.9240 |
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- | 0.21 | 0.84 | 500 | 0.1416 | 0.9635 |
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- | 0.0873 | 1.01 | 600 | 0.1724 | 0.9467 |
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- | 0.1594 | 1.17 | 700 | 0.2439 | 0.9320 |
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- | 0.0824 | 1.34 | 800 | 0.1039 | 0.9702 |
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- | 0.0888 | 1.51 | 900 | 0.1496 | 0.9547 |
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- | 0.1058 | 1.68 | 1000 | 0.1100 | 0.9748 |
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- | 0.077 | 1.85 | 1100 | 0.0784 | 0.9757 |
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- | 0.0486 | 2.01 | 1200 | 0.0997 | 0.9710 |
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- | 0.0087 | 2.18 | 1300 | 0.0704 | 0.9811 |
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- | 0.0382 | 2.35 | 1400 | 0.0910 | 0.9761 |
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- | 0.046 | 2.52 | 1500 | 0.0588 | 0.9840 |
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- | 0.0163 | 2.68 | 1600 | 0.0609 | 0.9836 |
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- | 0.0876 | 2.85 | 1700 | 0.0689 | 0.9811 |
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- | 0.0072 | 3.02 | 1800 | 0.0632 | 0.9861 |
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- | 0.0022 | 3.19 | 1900 | 0.0670 | 0.9845 |
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- | 0.0018 | 3.36 | 2000 | 0.0705 | 0.9845 |
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- | 0.0023 | 3.52 | 2100 | 0.0588 | 0.9861 |
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- | 0.0017 | 3.69 | 2200 | 0.0490 | 0.9882 |
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- | 0.0248 | 3.86 | 2300 | 0.0498 | 0.9882 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  # vit-base-beans-demo-v5
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0473
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+ - Accuracy: 0.9899
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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: 0.0001
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  - train_batch_size: 16
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  - eval_batch_size: 8
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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: linear
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+ - num_epochs: 6
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.8306 | 0.15 | 100 | 0.7915 | 0.9085 |
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+ | 0.3828 | 0.3 | 200 | 0.4295 | 0.9270 |
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+ | 0.3623 | 0.45 | 300 | 0.3022 | 0.9370 |
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+ | 0.2623 | 0.6 | 400 | 0.2883 | 0.9270 |
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+ | 0.2251 | 0.75 | 500 | 0.2303 | 0.9463 |
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+ | 0.2695 | 0.9 | 600 | 0.2465 | 0.9379 |
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+ | 0.1256 | 1.04 | 700 | 0.1249 | 0.9715 |
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+ | 0.0718 | 1.19 | 800 | 0.1335 | 0.9622 |
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+ | 0.0436 | 1.34 | 900 | 0.0989 | 0.9790 |
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+ | 0.1681 | 1.49 | 1000 | 0.1853 | 0.9496 |
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+ | 0.0771 | 1.64 | 1100 | 0.1400 | 0.9647 |
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+ | 0.0815 | 1.79 | 1200 | 0.0970 | 0.9765 |
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+ | 0.0203 | 1.94 | 1300 | 0.1408 | 0.9647 |
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+ | 0.0774 | 2.09 | 1400 | 0.1239 | 0.9664 |
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+ | 0.0723 | 2.24 | 1500 | 0.0623 | 0.9824 |
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+ | 0.0187 | 2.39 | 1600 | 0.0926 | 0.9731 |
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+ | 0.055 | 2.54 | 1700 | 0.0910 | 0.9757 |
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+ | 0.0304 | 2.69 | 1800 | 0.1278 | 0.9723 |
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+ | 0.0068 | 2.84 | 1900 | 0.1283 | 0.9681 |
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+ | 0.0319 | 2.99 | 2000 | 0.0590 | 0.9866 |
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+ | 0.0054 | 3.13 | 2100 | 0.0761 | 0.9824 |
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+ | 0.0136 | 3.28 | 2200 | 0.0759 | 0.9824 |
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+ | 0.0136 | 3.43 | 2300 | 0.0448 | 0.9866 |
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+ | 0.0086 | 3.58 | 2400 | 0.0816 | 0.9849 |
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+ | 0.0212 | 3.73 | 2500 | 0.0574 | 0.9849 |
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+ | 0.0392 | 3.88 | 2600 | 0.0765 | 0.9824 |
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+ | 0.0029 | 4.03 | 2700 | 0.0740 | 0.9790 |
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+ | 0.0521 | 4.18 | 2800 | 0.0349 | 0.9924 |
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+ | 0.0025 | 4.33 | 2900 | 0.0639 | 0.9866 |
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+ | 0.0024 | 4.48 | 3000 | 0.0509 | 0.9882 |
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+ | 0.0024 | 4.63 | 3100 | 0.0468 | 0.9899 |
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+ | 0.0024 | 4.78 | 3200 | 0.0524 | 0.9882 |
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+ | 0.0022 | 4.93 | 3300 | 0.0504 | 0.9899 |
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+ | 0.0022 | 5.07 | 3400 | 0.0488 | 0.9891 |
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+ | 0.0021 | 5.22 | 3500 | 0.0484 | 0.9899 |
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+ | 0.0019 | 5.37 | 3600 | 0.0480 | 0.9899 |
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+ | 0.0019 | 5.52 | 3700 | 0.0477 | 0.9899 |
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+ | 0.0019 | 5.67 | 3800 | 0.0475 | 0.9899 |
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+ | 0.0018 | 5.82 | 3900 | 0.0474 | 0.9899 |
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+ | 0.0018 | 5.97 | 4000 | 0.0473 | 0.9899 |
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  ### Framework versions
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