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HorcruxNo13
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vit-base-patch16-224

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +39 -39
  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: 0.7766666666666666
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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
@@ -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: 0.5054
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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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@@ -78,41 +78,41 @@ The following hyperparameters were used during training:
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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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- - 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:
25
  - name: Accuracy
26
  type: accuracy
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+ value: 0.8566666666666667
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  - name: Precision
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  type: precision
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+ value: 0.8522571872571872
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  - name: Recall
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  type: recall
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+ value: 0.8566666666666667
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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: 0.4410
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+ - Accuracy: 0.8567
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+ - Precision: 0.8523
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+ - Recall: 0.8567
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+ - F1 Score: 0.8517
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  ## Model description
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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 | 0.5841 | 0.7333 | 0.6770 | 0.7333 | 0.6479 |
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+ | No log | 2.0 | 8 | 0.5727 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 3.0 | 12 | 0.6089 | 0.7208 | 0.7222 | 0.7208 | 0.7215 |
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+ | No log | 4.0 | 16 | 0.5332 | 0.7458 | 0.7205 | 0.7458 | 0.6727 |
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+ | No log | 5.0 | 20 | 0.5314 | 0.7625 | 0.7410 | 0.7625 | 0.7416 |
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+ | No log | 6.0 | 24 | 0.5284 | 0.7583 | 0.7486 | 0.7583 | 0.6959 |
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+ | No log | 7.0 | 28 | 0.5220 | 0.775 | 0.7700 | 0.775 | 0.7286 |
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+ | 0.5564 | 8.0 | 32 | 0.5204 | 0.7833 | 0.7740 | 0.7833 | 0.7481 |
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+ | 0.5564 | 9.0 | 36 | 0.5044 | 0.7708 | 0.7616 | 0.7708 | 0.7650 |
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+ | 0.5564 | 10.0 | 40 | 0.4845 | 0.8125 | 0.8051 | 0.8125 | 0.7941 |
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+ | 0.5564 | 11.0 | 44 | 0.4921 | 0.7833 | 0.7726 | 0.7833 | 0.7757 |
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+ | 0.5564 | 12.0 | 48 | 0.4792 | 0.8167 | 0.8098 | 0.8167 | 0.7996 |
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+ | 0.5564 | 13.0 | 52 | 0.4825 | 0.8 | 0.7889 | 0.8 | 0.7901 |
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+ | 0.5564 | 14.0 | 56 | 0.4987 | 0.8083 | 0.7989 | 0.8083 | 0.8002 |
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+ | 0.3176 | 15.0 | 60 | 0.4970 | 0.8208 | 0.8144 | 0.8208 | 0.8050 |
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+ | 0.3176 | 16.0 | 64 | 0.5076 | 0.8083 | 0.7983 | 0.8083 | 0.7923 |
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+ | 0.3176 | 17.0 | 68 | 0.5227 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
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+ | 0.3176 | 18.0 | 72 | 0.5132 | 0.8042 | 0.7928 | 0.8042 | 0.7905 |
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+ | 0.3176 | 19.0 | 76 | 0.5081 | 0.8167 | 0.8087 | 0.8167 | 0.8014 |
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+ | 0.3176 | 20.0 | 80 | 0.5140 | 0.8292 | 0.8220 | 0.8292 | 0.8187 |
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+ | 0.3176 | 21.0 | 84 | 0.5392 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
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+ | 0.3176 | 22.0 | 88 | 0.5175 | 0.7958 | 0.7829 | 0.7958 | 0.7815 |
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+ | 0.1778 | 23.0 | 92 | 0.5109 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
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+ | 0.1778 | 24.0 | 96 | 0.4961 | 0.8292 | 0.8217 | 0.8292 | 0.8213 |
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+ | 0.1778 | 25.0 | 100 | 0.5251 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
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+ | 0.1778 | 26.0 | 104 | 0.5192 | 0.8167 | 0.8075 | 0.8167 | 0.8046 |
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+ | 0.1778 | 27.0 | 108 | 0.5030 | 0.8333 | 0.8274 | 0.8333 | 0.8286 |
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+ | 0.1778 | 28.0 | 112 | 0.5031 | 0.8375 | 0.8310 | 0.8375 | 0.8300 |
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+ | 0.1778 | 29.0 | 116 | 0.5164 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
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+ | 0.1109 | 30.0 | 120 | 0.5192 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
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  ### Framework versions
114
 
115
+ - Transformers 4.33.3
116
  - Pytorch 2.0.1+cu118
117
  - Datasets 2.14.5
118
  - Tokenizers 0.13.3
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