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vit-base-patch16-224

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results (legacy)
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  1. README.md +39 -24
  2. pytorch_model.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: 0.7833333333333333
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  - name: Precision
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  type: precision
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- value: 0.7701923076923076
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  - name: Recall
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  type: recall
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- value: 0.7833333333333333
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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
@@ -40,11 +40,11 @@ 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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4772
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- - Accuracy: 0.7833
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- - Precision: 0.7702
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- - Recall: 0.7833
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- - F1 Score: 0.7559
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  ## Model description
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@@ -72,27 +72,42 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.6010 | 0.7333 | 0.6725 | 0.7333 | 0.6280 |
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- | No log | 2.0 | 8 | 0.5552 | 0.7375 | 0.8067 | 0.7375 | 0.6302 |
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- | No log | 3.0 | 12 | 0.5450 | 0.7542 | 0.7598 | 0.7542 | 0.6782 |
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- | 0.576 | 4.0 | 16 | 0.5325 | 0.75 | 0.7707 | 0.75 | 0.6641 |
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- | 0.576 | 5.0 | 20 | 0.5234 | 0.75 | 0.7232 | 0.75 | 0.6900 |
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- | 0.576 | 6.0 | 24 | 0.5112 | 0.7625 | 0.7506 | 0.7625 | 0.7076 |
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- | 0.576 | 7.0 | 28 | 0.5082 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
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- | 0.4876 | 8.0 | 32 | 0.5067 | 0.7667 | 0.7466 | 0.7667 | 0.7288 |
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- | 0.4876 | 9.0 | 36 | 0.5091 | 0.7792 | 0.7623 | 0.7792 | 0.7528 |
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- | 0.4876 | 10.0 | 40 | 0.5023 | 0.7583 | 0.7393 | 0.7583 | 0.7045 |
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- | 0.4876 | 11.0 | 44 | 0.4911 | 0.7708 | 0.7507 | 0.7708 | 0.7435 |
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- | 0.4379 | 12.0 | 48 | 0.4921 | 0.7667 | 0.7487 | 0.7667 | 0.7513 |
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- | 0.4379 | 13.0 | 52 | 0.4906 | 0.7917 | 0.7792 | 0.7917 | 0.7680 |
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- | 0.4379 | 14.0 | 56 | 0.4919 | 0.7875 | 0.7731 | 0.7875 | 0.7645 |
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- | 0.4003 | 15.0 | 60 | 0.4929 | 0.7833 | 0.7678 | 0.7833 | 0.7587 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7766666666666666
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  - name: Precision
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  type: precision
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+ value: 0.7660774253731343
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  - name: Recall
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  type: recall
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+ value: 0.7766666666666666
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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
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5054
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+ - Accuracy: 0.7767
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+ - Precision: 0.7661
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+ - Recall: 0.7767
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+ - F1 Score: 0.7395
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 30
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.6002 | 0.7208 | 0.6144 | 0.7208 | 0.6282 |
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+ | No log | 2.0 | 8 | 0.5620 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 3.0 | 12 | 0.5641 | 0.7208 | 0.6144 | 0.7208 | 0.6282 |
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+ | No log | 4.0 | 16 | 0.5504 | 0.7208 | 0.6453 | 0.7208 | 0.6460 |
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+ | No log | 5.0 | 20 | 0.5444 | 0.7292 | 0.6795 | 0.7292 | 0.6754 |
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+ | No log | 6.0 | 24 | 0.5713 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 7.0 | 28 | 0.5488 | 0.7375 | 0.8067 | 0.7375 | 0.6302 |
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+ | 0.5813 | 8.0 | 32 | 0.5408 | 0.7417 | 0.8090 | 0.7417 | 0.6397 |
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+ | 0.5813 | 9.0 | 36 | 0.5387 | 0.7542 | 0.7292 | 0.7542 | 0.7015 |
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+ | 0.5813 | 10.0 | 40 | 0.5314 | 0.75 | 0.7212 | 0.75 | 0.6943 |
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+ | 0.5813 | 11.0 | 44 | 0.5283 | 0.7792 | 0.7813 | 0.7792 | 0.7318 |
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+ | 0.5813 | 12.0 | 48 | 0.5227 | 0.7667 | 0.7819 | 0.7667 | 0.7019 |
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+ | 0.5813 | 13.0 | 52 | 0.5283 | 0.7583 | 0.7336 | 0.7583 | 0.7308 |
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+ | 0.5813 | 14.0 | 56 | 0.5263 | 0.7583 | 0.7393 | 0.7583 | 0.7045 |
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+ | 0.5092 | 15.0 | 60 | 0.5205 | 0.7667 | 0.7819 | 0.7667 | 0.7019 |
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+ | 0.5092 | 16.0 | 64 | 0.5236 | 0.7625 | 0.8206 | 0.7625 | 0.6837 |
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+ | 0.5092 | 17.0 | 68 | 0.5241 | 0.7667 | 0.8230 | 0.7667 | 0.6919 |
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+ | 0.5092 | 18.0 | 72 | 0.4962 | 0.7708 | 0.7639 | 0.7708 | 0.7217 |
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+ | 0.5092 | 19.0 | 76 | 0.4942 | 0.7708 | 0.7878 | 0.7708 | 0.7094 |
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+ | 0.5092 | 20.0 | 80 | 0.4909 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
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+ | 0.5092 | 21.0 | 84 | 0.4964 | 0.7583 | 0.7343 | 0.7583 | 0.7334 |
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+ | 0.5092 | 22.0 | 88 | 0.4928 | 0.7583 | 0.7393 | 0.7583 | 0.7045 |
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+ | 0.4804 | 23.0 | 92 | 0.4938 | 0.7542 | 0.7292 | 0.7542 | 0.7015 |
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+ | 0.4804 | 24.0 | 96 | 0.4949 | 0.7583 | 0.7327 | 0.7583 | 0.7253 |
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+ | 0.4804 | 25.0 | 100 | 0.4946 | 0.7542 | 0.7268 | 0.7542 | 0.7220 |
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+ | 0.4804 | 26.0 | 104 | 0.4935 | 0.7583 | 0.7330 | 0.7583 | 0.7281 |
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+ | 0.4804 | 27.0 | 108 | 0.4927 | 0.7542 | 0.7268 | 0.7542 | 0.7220 |
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+ | 0.4804 | 28.0 | 112 | 0.4943 | 0.7542 | 0.7268 | 0.7542 | 0.7220 |
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+ | 0.4804 | 29.0 | 116 | 0.4951 | 0.7542 | 0.7268 | 0.7542 | 0.7220 |
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+ | 0.4442 | 30.0 | 120 | 0.4952 | 0.75 | 0.7203 | 0.75 | 0.7158 |
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  ### Framework versions
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