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HorcruxNo13
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beit-base-patch16-224

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
beit
Generated from Trainer
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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: 0.8966666666666666
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  - name: Precision
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  type: precision
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- value: 0.891224605606628
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  - name: Recall
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  type: recall
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- value: 0.8966666666666666
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-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: 0.2426
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- - Accuracy: 0.8967
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- - Precision: 0.8912
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- - Recall: 0.8967
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- - F1 Score: 0.8935
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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 | 0.4160 | 0.8667 | 0.8037 | 0.8667 | 0.8160 |
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- | No log | 2.0 | 8 | 0.4441 | 0.8375 | 0.7702 | 0.8375 | 0.7998 |
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- | No log | 3.0 | 12 | 0.4451 | 0.8667 | 0.8559 | 0.8667 | 0.8605 |
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- | 0.4959 | 4.0 | 16 | 0.3299 | 0.8792 | 0.8545 | 0.8792 | 0.8551 |
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- | 0.4959 | 5.0 | 20 | 0.3813 | 0.8458 | 0.8776 | 0.8458 | 0.8580 |
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- | 0.4959 | 6.0 | 24 | 0.2802 | 0.8958 | 0.8851 | 0.8958 | 0.8881 |
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- | 0.4959 | 7.0 | 28 | 0.2991 | 0.8875 | 0.8830 | 0.8875 | 0.8850 |
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- | 0.3696 | 8.0 | 32 | 0.2565 | 0.8917 | 0.8792 | 0.8917 | 0.8825 |
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- | 0.3696 | 9.0 | 36 | 0.2582 | 0.9 | 0.8949 | 0.9 | 0.8970 |
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- | 0.3696 | 10.0 | 40 | 0.2472 | 0.9 | 0.8927 | 0.9 | 0.8954 |
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- | 0.3696 | 11.0 | 44 | 0.2463 | 0.9208 | 0.9179 | 0.9208 | 0.9191 |
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- | 0.3299 | 12.0 | 48 | 0.2474 | 0.9167 | 0.9145 | 0.9167 | 0.9155 |
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- | 0.3299 | 13.0 | 52 | 0.2826 | 0.8833 | 0.8971 | 0.8833 | 0.8889 |
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- | 0.3299 | 14.0 | 56 | 0.2720 | 0.8958 | 0.9035 | 0.8958 | 0.8991 |
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- | 0.3036 | 15.0 | 60 | 0.2629 | 0.9 | 0.9059 | 0.9 | 0.9025 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.85
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  - name: Precision
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  type: precision
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+ value: 0.8455590062111802
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  - name: Recall
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  type: recall
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+ value: 0.85
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-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: 0.4871
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+ - Accuracy: 0.85
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+ - Precision: 0.8456
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+ - Recall: 0.85
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+ - F1 Score: 0.8464
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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 | 0.5784 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 2.0 | 8 | 0.5813 | 0.7375 | 0.7030 | 0.7375 | 0.6441 |
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+ | No log | 3.0 | 12 | 0.5486 | 0.7417 | 0.7297 | 0.7417 | 0.7343 |
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+ | No log | 4.0 | 16 | 0.5394 | 0.7542 | 0.7333 | 0.7542 | 0.7370 |
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+ | No log | 5.0 | 20 | 0.5067 | 0.775 | 0.7658 | 0.775 | 0.7321 |
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+ | No log | 6.0 | 24 | 0.5542 | 0.7958 | 0.7966 | 0.7958 | 0.7613 |
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+ | No log | 7.0 | 28 | 0.4753 | 0.7958 | 0.7834 | 0.7958 | 0.7758 |
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+ | 0.5325 | 8.0 | 32 | 0.5265 | 0.7792 | 0.7661 | 0.7792 | 0.7448 |
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+ | 0.5325 | 9.0 | 36 | 0.4789 | 0.8208 | 0.8134 | 0.8208 | 0.8067 |
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+ | 0.5325 | 10.0 | 40 | 0.4939 | 0.7875 | 0.7932 | 0.7875 | 0.7900 |
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+ | 0.5325 | 11.0 | 44 | 0.4917 | 0.8042 | 0.8032 | 0.8042 | 0.8037 |
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+ | 0.5325 | 12.0 | 48 | 0.5001 | 0.8083 | 0.8019 | 0.8083 | 0.8041 |
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+ | 0.5325 | 13.0 | 52 | 0.4742 | 0.8 | 0.7897 | 0.8 | 0.7915 |
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+ | 0.5325 | 14.0 | 56 | 0.5439 | 0.7875 | 0.8037 | 0.7875 | 0.7932 |
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+ | 0.3381 | 15.0 | 60 | 0.5436 | 0.8333 | 0.8265 | 0.8333 | 0.8263 |
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+ | 0.3381 | 16.0 | 64 | 0.4989 | 0.8375 | 0.8312 | 0.8375 | 0.8288 |
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+ | 0.3381 | 17.0 | 68 | 0.4949 | 0.8333 | 0.8282 | 0.8333 | 0.8296 |
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+ | 0.3381 | 18.0 | 72 | 0.4709 | 0.8292 | 0.8283 | 0.8292 | 0.8287 |
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+ | 0.3381 | 19.0 | 76 | 0.4680 | 0.8167 | 0.8133 | 0.8167 | 0.8147 |
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+ | 0.3381 | 20.0 | 80 | 0.5053 | 0.8417 | 0.8362 | 0.8417 | 0.8371 |
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+ | 0.3381 | 21.0 | 84 | 0.5480 | 0.8458 | 0.8459 | 0.8458 | 0.8322 |
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+ | 0.3381 | 22.0 | 88 | 0.4548 | 0.8542 | 0.8512 | 0.8542 | 0.8522 |
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+ | 0.2076 | 23.0 | 92 | 0.4891 | 0.8458 | 0.8407 | 0.8458 | 0.8376 |
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+ | 0.2076 | 24.0 | 96 | 0.4981 | 0.85 | 0.8486 | 0.85 | 0.8492 |
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+ | 0.2076 | 25.0 | 100 | 0.4993 | 0.8458 | 0.8426 | 0.8458 | 0.8438 |
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+ | 0.2076 | 26.0 | 104 | 0.5026 | 0.8542 | 0.8503 | 0.8542 | 0.8514 |
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+ | 0.2076 | 27.0 | 108 | 0.4944 | 0.8542 | 0.8522 | 0.8542 | 0.8530 |
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+ | 0.2076 | 28.0 | 112 | 0.4821 | 0.8542 | 0.8549 | 0.8542 | 0.8545 |
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+ | 0.2076 | 29.0 | 116 | 0.4714 | 0.8583 | 0.8559 | 0.8583 | 0.8568 |
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+ | 0.138 | 30.0 | 120 | 0.4705 | 0.8583 | 0.8559 | 0.8583 | 0.8568 |
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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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