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HorcruxNo13
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pvt-tiny-224

Image Classification
Transformers
PyTorch
pvt
Generated from Trainer
Eval Results
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  1. README.md +40 -25
  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: 0.8866666666666667
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  - name: Precision
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  type: precision
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- value: 0.8696446479180292
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  - name: Recall
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  type: recall
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- value: 0.8866666666666667
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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 [Zetatech/pvt-tiny-224](https://huggingface.co/Zetatech/pvt-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3101
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- - Accuracy: 0.8867
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- - Precision: 0.8696
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- - Recall: 0.8867
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- - F1 Score: 0.8732
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  ## Model description
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@@ -72,32 +72,47 @@ 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 | 0.3871 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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- | No log | 2.0 | 8 | 0.3643 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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- | No log | 3.0 | 12 | 0.3967 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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- | 0.4822 | 4.0 | 16 | 0.3791 | 0.8708 | 0.8256 | 0.8708 | 0.8183 |
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- | 0.4822 | 5.0 | 20 | 0.5400 | 0.7583 | 0.8645 | 0.7583 | 0.7929 |
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- | 0.4822 | 6.0 | 24 | 0.3422 | 0.8958 | 0.8835 | 0.8958 | 0.8859 |
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- | 0.4822 | 7.0 | 28 | 0.3987 | 0.8583 | 0.8782 | 0.8583 | 0.8664 |
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- | 0.3927 | 8.0 | 32 | 0.2988 | 0.9125 | 0.9059 | 0.9125 | 0.9077 |
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- | 0.3927 | 9.0 | 36 | 0.2899 | 0.9083 | 0.9005 | 0.9083 | 0.9024 |
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- | 0.3927 | 10.0 | 40 | 0.2881 | 0.9083 | 0.9005 | 0.9083 | 0.9024 |
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- | 0.3927 | 11.0 | 44 | 0.2787 | 0.9125 | 0.9059 | 0.9125 | 0.9077 |
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- | 0.3561 | 12.0 | 48 | 0.2923 | 0.8958 | 0.8973 | 0.8958 | 0.8965 |
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- | 0.3561 | 13.0 | 52 | 0.2960 | 0.9042 | 0.9055 | 0.9042 | 0.9048 |
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- | 0.3561 | 14.0 | 56 | 0.3073 | 0.8958 | 0.9003 | 0.8958 | 0.8979 |
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- | 0.3382 | 15.0 | 60 | 0.3018 | 0.9 | 0.9028 | 0.9 | 0.9013 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.33.2
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
 
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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: 0.7680555555555556
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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
 
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  This model is a fine-tuned version of [Zetatech/pvt-tiny-224](https://huggingface.co/Zetatech/pvt-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4869
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+ - Accuracy: 0.7833
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+ - Precision: 0.7681
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+ - Recall: 0.7833
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+ - F1 Score: 0.7632
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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 | 0.5984 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 2.0 | 8 | 0.6103 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 3.0 | 12 | 0.5861 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 4.0 | 16 | 0.5478 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 5.0 | 20 | 0.5961 | 0.725 | 0.7119 | 0.725 | 0.7171 |
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+ | No log | 6.0 | 24 | 0.5317 | 0.7542 | 0.7261 | 0.7542 | 0.7159 |
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+ | No log | 7.0 | 28 | 0.5620 | 0.7458 | 0.7289 | 0.7458 | 0.7342 |
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+ | 0.5878 | 8.0 | 32 | 0.5281 | 0.7542 | 0.7316 | 0.7542 | 0.6973 |
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+ | 0.5878 | 9.0 | 36 | 0.5434 | 0.7625 | 0.7395 | 0.7625 | 0.7368 |
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+ | 0.5878 | 10.0 | 40 | 0.5236 | 0.775 | 0.7658 | 0.775 | 0.7321 |
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+ | 0.5878 | 11.0 | 44 | 0.5411 | 0.7542 | 0.7382 | 0.7542 | 0.7429 |
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+ | 0.5878 | 12.0 | 48 | 0.5186 | 0.7708 | 0.7507 | 0.7708 | 0.7460 |
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+ | 0.5878 | 13.0 | 52 | 0.5194 | 0.7667 | 0.7500 | 0.7667 | 0.7533 |
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+ | 0.5878 | 14.0 | 56 | 0.5049 | 0.7875 | 0.7739 | 0.7875 | 0.7621 |
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+ | 0.4973 | 15.0 | 60 | 0.5125 | 0.7833 | 0.7691 | 0.7833 | 0.7709 |
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+ | 0.4973 | 16.0 | 64 | 0.5000 | 0.7917 | 0.7804 | 0.7917 | 0.7656 |
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+ | 0.4973 | 17.0 | 68 | 0.5137 | 0.7583 | 0.7560 | 0.7583 | 0.7571 |
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+ | 0.4973 | 18.0 | 72 | 0.4833 | 0.8 | 0.788 | 0.8 | 0.7833 |
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+ | 0.4973 | 19.0 | 76 | 0.4929 | 0.7917 | 0.7816 | 0.7917 | 0.7843 |
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+ | 0.4973 | 20.0 | 80 | 0.4858 | 0.8042 | 0.7930 | 0.8042 | 0.7887 |
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+ | 0.4973 | 21.0 | 84 | 0.4900 | 0.7917 | 0.7777 | 0.7917 | 0.7743 |
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+ | 0.4973 | 22.0 | 88 | 0.4886 | 0.7958 | 0.7829 | 0.7958 | 0.7815 |
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+ | 0.439 | 23.0 | 92 | 0.4841 | 0.7917 | 0.7778 | 0.7917 | 0.7723 |
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+ | 0.439 | 24.0 | 96 | 0.4855 | 0.8 | 0.7883 | 0.8 | 0.7885 |
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+ | 0.439 | 25.0 | 100 | 0.4856 | 0.8 | 0.7879 | 0.8 | 0.7869 |
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+ | 0.439 | 26.0 | 104 | 0.4839 | 0.8 | 0.7879 | 0.8 | 0.7869 |
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+ | 0.439 | 27.0 | 108 | 0.4811 | 0.8 | 0.7879 | 0.8 | 0.7869 |
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+ | 0.439 | 28.0 | 112 | 0.4834 | 0.8 | 0.7889 | 0.8 | 0.7901 |
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+ | 0.439 | 29.0 | 116 | 0.4839 | 0.8 | 0.7889 | 0.8 | 0.7901 |
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+ | 0.4092 | 30.0 | 120 | 0.4838 | 0.8 | 0.7889 | 0.8 | 0.7901 |
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  ### Framework versions
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+ - Transformers 4.33.3
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
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