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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 +59 -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: 0.78
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  - name: Precision
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  type: precision
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- value: 0.781535758027584
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  - name: Recall
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  type: recall
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- value: 0.78
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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: 0.4819
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- - Accuracy: 0.78
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- - Precision: 0.7815
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- - Recall: 0.78
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- - F1 Score: 0.7807
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  ## Model description
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@@ -72,42 +72,62 @@ 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: 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 | 0.5936 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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- | No log | 2.0 | 8 | 0.5702 | 0.7208 | 0.6468 | 0.7208 | 0.6283 |
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- | No log | 3.0 | 12 | 0.5834 | 0.7125 | 0.6933 | 0.7125 | 0.7000 |
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- | No log | 4.0 | 16 | 0.5471 | 0.7375 | 0.7034 | 0.7375 | 0.6846 |
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- | No log | 5.0 | 20 | 0.5487 | 0.725 | 0.6938 | 0.725 | 0.6982 |
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- | No log | 6.0 | 24 | 0.5253 | 0.7458 | 0.7182 | 0.7458 | 0.7116 |
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- | No log | 7.0 | 28 | 0.5556 | 0.7417 | 0.7393 | 0.7417 | 0.7404 |
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- | 0.5648 | 8.0 | 32 | 0.5183 | 0.7417 | 0.7155 | 0.7417 | 0.7165 |
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- | 0.5648 | 9.0 | 36 | 0.5159 | 0.7667 | 0.7504 | 0.7667 | 0.7522 |
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- | 0.5648 | 10.0 | 40 | 0.5137 | 0.7708 | 0.7579 | 0.7708 | 0.7609 |
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- | 0.5648 | 11.0 | 44 | 0.5014 | 0.7833 | 0.7693 | 0.7833 | 0.7643 |
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- | 0.5648 | 12.0 | 48 | 0.5157 | 0.75 | 0.7524 | 0.75 | 0.7511 |
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- | 0.5648 | 13.0 | 52 | 0.5151 | 0.7417 | 0.7441 | 0.7417 | 0.7428 |
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- | 0.5648 | 14.0 | 56 | 0.4908 | 0.7792 | 0.7653 | 0.7792 | 0.7663 |
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- | 0.3814 | 15.0 | 60 | 0.4901 | 0.7833 | 0.7723 | 0.7833 | 0.7747 |
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- | 0.3814 | 16.0 | 64 | 0.4993 | 0.7667 | 0.7689 | 0.7667 | 0.7677 |
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- | 0.3814 | 17.0 | 68 | 0.4814 | 0.7792 | 0.7642 | 0.7792 | 0.7627 |
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- | 0.3814 | 18.0 | 72 | 0.5165 | 0.7583 | 0.7796 | 0.7583 | 0.7656 |
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- | 0.3814 | 19.0 | 76 | 0.4817 | 0.7958 | 0.7915 | 0.7958 | 0.7933 |
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- | 0.3814 | 20.0 | 80 | 0.4748 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
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- | 0.3814 | 21.0 | 84 | 0.4831 | 0.8042 | 0.8033 | 0.8042 | 0.8037 |
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- | 0.3814 | 22.0 | 88 | 0.4795 | 0.8083 | 0.8013 | 0.8083 | 0.8032 |
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- | 0.2354 | 23.0 | 92 | 0.5048 | 0.7708 | 0.7790 | 0.7708 | 0.7743 |
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- | 0.2354 | 24.0 | 96 | 0.4838 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
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- | 0.2354 | 25.0 | 100 | 0.4894 | 0.7833 | 0.7833 | 0.7833 | 0.7833 |
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- | 0.2354 | 26.0 | 104 | 0.4852 | 0.8 | 0.7914 | 0.8 | 0.7933 |
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- | 0.2354 | 27.0 | 108 | 0.4882 | 0.8 | 0.7982 | 0.8 | 0.7990 |
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- | 0.2354 | 28.0 | 112 | 0.4932 | 0.7875 | 0.7929 | 0.7875 | 0.7898 |
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- | 0.2354 | 29.0 | 116 | 0.4883 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
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- | 0.1479 | 30.0 | 120 | 0.4886 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.79
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  - name: Precision
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  type: precision
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+ value: 0.7955164222268126
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  - name: Recall
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  type: recall
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+ value: 0.79
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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: 0.6740
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+ - Accuracy: 0.79
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+ - Precision: 0.7955
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+ - Recall: 0.79
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+ - F1 Score: 0.7923
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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: 50
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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 | 0.5895 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | No log | 2.0 | 8 | 0.5737 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | No log | 3.0 | 12 | 0.5746 | 0.7333 | 0.6978 | 0.7333 | 0.6589 |
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+ | No log | 4.0 | 16 | 0.5449 | 0.7292 | 0.7126 | 0.7292 | 0.6263 |
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+ | No log | 5.0 | 20 | 0.5943 | 0.7208 | 0.7362 | 0.7208 | 0.7270 |
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+ | No log | 6.0 | 24 | 0.5124 | 0.75 | 0.7360 | 0.75 | 0.6895 |
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+ | No log | 7.0 | 28 | 0.6057 | 0.6625 | 0.7301 | 0.6625 | 0.6797 |
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+ | No log | 8.0 | 32 | 0.5059 | 0.7583 | 0.7376 | 0.7583 | 0.7214 |
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+ | No log | 9.0 | 36 | 0.5734 | 0.7125 | 0.7474 | 0.7125 | 0.7237 |
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+ | No log | 10.0 | 40 | 0.5069 | 0.7458 | 0.7182 | 0.7458 | 0.7116 |
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+ | No log | 11.0 | 44 | 0.5135 | 0.775 | 0.7659 | 0.775 | 0.7689 |
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+ | No log | 12.0 | 48 | 0.4943 | 0.775 | 0.7601 | 0.775 | 0.7610 |
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+ | 0.5275 | 13.0 | 52 | 0.5654 | 0.7458 | 0.7790 | 0.7458 | 0.7557 |
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+ | 0.5275 | 14.0 | 56 | 0.5257 | 0.7625 | 0.7636 | 0.7625 | 0.7631 |
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+ | 0.5275 | 15.0 | 60 | 0.5107 | 0.7875 | 0.7813 | 0.7875 | 0.7836 |
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+ | 0.5275 | 16.0 | 64 | 0.5514 | 0.7333 | 0.7655 | 0.7333 | 0.7434 |
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+ | 0.5275 | 17.0 | 68 | 0.5004 | 0.7833 | 0.7698 | 0.7833 | 0.7699 |
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+ | 0.5275 | 18.0 | 72 | 0.5999 | 0.7125 | 0.7738 | 0.7125 | 0.7269 |
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+ | 0.5275 | 19.0 | 76 | 0.4975 | 0.7667 | 0.7554 | 0.7667 | 0.7589 |
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+ | 0.5275 | 20.0 | 80 | 0.5120 | 0.7917 | 0.7981 | 0.7917 | 0.7944 |
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+ | 0.5275 | 21.0 | 84 | 0.5203 | 0.7833 | 0.7876 | 0.7833 | 0.7853 |
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+ | 0.5275 | 22.0 | 88 | 0.5304 | 0.8042 | 0.8051 | 0.8042 | 0.8046 |
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+ | 0.5275 | 23.0 | 92 | 0.5475 | 0.825 | 0.825 | 0.825 | 0.8250 |
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+ | 0.5275 | 24.0 | 96 | 0.5757 | 0.7458 | 0.7661 | 0.7458 | 0.7531 |
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+ | 0.2422 | 25.0 | 100 | 0.5669 | 0.7875 | 0.7829 | 0.7875 | 0.7848 |
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+ | 0.2422 | 26.0 | 104 | 0.5489 | 0.7958 | 0.7931 | 0.7958 | 0.7943 |
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+ | 0.2422 | 27.0 | 108 | 0.5372 | 0.8 | 0.7982 | 0.8 | 0.7990 |
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+ | 0.2422 | 28.0 | 112 | 0.5500 | 0.8208 | 0.8160 | 0.8208 | 0.8176 |
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+ | 0.2422 | 29.0 | 116 | 0.5682 | 0.8042 | 0.8033 | 0.8042 | 0.8037 |
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+ | 0.2422 | 30.0 | 120 | 0.5899 | 0.8083 | 0.8050 | 0.8083 | 0.8064 |
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+ | 0.2422 | 31.0 | 124 | 0.6217 | 0.8 | 0.8063 | 0.8 | 0.8026 |
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+ | 0.2422 | 32.0 | 128 | 0.6063 | 0.8125 | 0.8053 | 0.8125 | 0.8068 |
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+ | 0.2422 | 33.0 | 132 | 0.5843 | 0.8042 | 0.8033 | 0.8042 | 0.8037 |
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+ | 0.2422 | 34.0 | 136 | 0.6020 | 0.8125 | 0.8073 | 0.8125 | 0.8091 |
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+ | 0.2422 | 35.0 | 140 | 0.6180 | 0.8042 | 0.8092 | 0.8042 | 0.8063 |
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+ | 0.2422 | 36.0 | 144 | 0.6287 | 0.8208 | 0.8171 | 0.8208 | 0.8186 |
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+ | 0.2422 | 37.0 | 148 | 0.6231 | 0.825 | 0.8234 | 0.825 | 0.8242 |
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+ | 0.0631 | 38.0 | 152 | 0.6260 | 0.8292 | 0.8300 | 0.8292 | 0.8296 |
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+ | 0.0631 | 39.0 | 156 | 0.6278 | 0.8333 | 0.8294 | 0.8333 | 0.8308 |
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+ | 0.0631 | 40.0 | 160 | 0.6325 | 0.8208 | 0.8200 | 0.8208 | 0.8204 |
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+ | 0.0631 | 41.0 | 164 | 0.6370 | 0.8083 | 0.8013 | 0.8083 | 0.8032 |
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+ | 0.0631 | 42.0 | 168 | 0.6371 | 0.8125 | 0.8100 | 0.8125 | 0.8111 |
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+ | 0.0631 | 43.0 | 172 | 0.6404 | 0.8042 | 0.8016 | 0.8042 | 0.8027 |
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+ | 0.0631 | 44.0 | 176 | 0.6640 | 0.8292 | 0.8227 | 0.8292 | 0.8229 |
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+ | 0.0631 | 45.0 | 180 | 0.6636 | 0.8208 | 0.8185 | 0.8208 | 0.8195 |
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+ | 0.0631 | 46.0 | 184 | 0.6826 | 0.8083 | 0.8122 | 0.8083 | 0.8100 |
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+ | 0.0631 | 47.0 | 188 | 0.6756 | 0.8208 | 0.8185 | 0.8208 | 0.8195 |
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+ | 0.0631 | 48.0 | 192 | 0.6695 | 0.8292 | 0.8246 | 0.8292 | 0.8261 |
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+ | 0.0631 | 49.0 | 196 | 0.6669 | 0.825 | 0.8198 | 0.825 | 0.8213 |
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+ | 0.0264 | 50.0 | 200 | 0.6658 | 0.825 | 0.8198 | 0.825 | 0.8213 |
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  ### Framework versions
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