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vit-msn-small-lateral_flow_ivalidation_train_test_7

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  1. README.md +101 -84
  2. model.safetensors +1 -1
  3. runs/Jan16_17-31-32_d2e708f84219/events.out.tfevents.1737048698.d2e708f84219.310.6 +2 -2
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8864468864468864
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3142
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- - Accuracy: 0.8864
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  ## Model description
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@@ -53,98 +53,115 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
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- - gradient_accumulation_steps: 5
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- - total_train_batch_size: 320
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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: 0.5
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  - num_epochs: 100
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | No log | 0.7692 | 2 | 0.4402 | 0.8791 |
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- | No log | 1.9231 | 5 | 0.4425 | 0.8571 |
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- | No log | 2.6923 | 7 | 0.4371 | 0.8571 |
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- | 0.3077 | 3.8462 | 10 | 0.4021 | 0.8608 |
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- | 0.3077 | 5.0 | 13 | 0.4551 | 0.7875 |
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- | 0.3077 | 5.7692 | 15 | 0.4252 | 0.8462 |
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- | 0.3077 | 6.9231 | 18 | 0.3545 | 0.8864 |
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- | 0.2902 | 7.6923 | 20 | 0.3323 | 0.9084 |
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- | 0.2902 | 8.8462 | 23 | 0.3734 | 0.8645 |
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- | 0.2902 | 10.0 | 26 | 0.3801 | 0.8571 |
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- | 0.2902 | 10.7692 | 28 | 0.4348 | 0.8205 |
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- | 0.3039 | 11.9231 | 31 | 0.4915 | 0.7582 |
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- | 0.3039 | 12.6923 | 33 | 0.3970 | 0.8315 |
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- | 0.3039 | 13.8462 | 36 | 0.4419 | 0.8059 |
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- | 0.3039 | 15.0 | 39 | 0.3413 | 0.8791 |
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- | 0.2603 | 15.7692 | 41 | 0.3510 | 0.8718 |
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- | 0.2603 | 16.9231 | 44 | 0.3650 | 0.8755 |
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- | 0.2603 | 17.6923 | 46 | 0.4008 | 0.8462 |
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- | 0.2603 | 18.8462 | 49 | 0.4626 | 0.7912 |
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- | 0.2589 | 20.0 | 52 | 0.3576 | 0.8608 |
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- | 0.2589 | 20.7692 | 54 | 0.3142 | 0.8864 |
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- | 0.2589 | 21.9231 | 57 | 0.3479 | 0.8718 |
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- | 0.2589 | 22.6923 | 59 | 0.5111 | 0.7546 |
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- | 0.2655 | 23.8462 | 62 | 0.4675 | 0.8059 |
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- | 0.2655 | 25.0 | 65 | 0.5117 | 0.7802 |
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- | 0.2655 | 25.7692 | 67 | 0.5082 | 0.8022 |
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- | 0.2591 | 26.9231 | 70 | 0.4156 | 0.8462 |
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- | 0.2591 | 27.6923 | 72 | 0.5523 | 0.7546 |
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- | 0.2591 | 28.8462 | 75 | 0.3372 | 0.8681 |
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- | 0.2591 | 30.0 | 78 | 0.5110 | 0.7875 |
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- | 0.2502 | 30.7692 | 80 | 0.4313 | 0.8242 |
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- | 0.2502 | 31.9231 | 83 | 0.3266 | 0.8718 |
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- | 0.2502 | 32.6923 | 85 | 0.3752 | 0.8571 |
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- | 0.2502 | 33.8462 | 88 | 0.3759 | 0.8608 |
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- | 0.2479 | 35.0 | 91 | 0.4897 | 0.7985 |
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- | 0.2479 | 35.7692 | 93 | 0.6559 | 0.7143 |
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- | 0.2479 | 36.9231 | 96 | 0.4390 | 0.8059 |
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- | 0.2479 | 37.6923 | 98 | 0.3943 | 0.8388 |
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- | 0.2874 | 38.8462 | 101 | 0.4982 | 0.7985 |
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- | 0.2874 | 40.0 | 104 | 0.5053 | 0.7839 |
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- | 0.2874 | 40.7692 | 106 | 0.3226 | 0.8645 |
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- | 0.2874 | 41.9231 | 109 | 0.5230 | 0.7766 |
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- | 0.2297 | 42.6923 | 111 | 0.3717 | 0.8571 |
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- | 0.2297 | 43.8462 | 114 | 0.3857 | 0.8535 |
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- | 0.2297 | 45.0 | 117 | 0.4100 | 0.8388 |
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- | 0.2297 | 45.7692 | 119 | 0.5891 | 0.7839 |
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- | 0.2214 | 46.9231 | 122 | 0.3930 | 0.8681 |
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- | 0.2214 | 47.6923 | 124 | 0.5677 | 0.7949 |
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- | 0.2214 | 48.8462 | 127 | 0.3372 | 0.8828 |
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- | 0.2135 | 50.0 | 130 | 0.5339 | 0.7875 |
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- | 0.2135 | 50.7692 | 132 | 0.5861 | 0.7546 |
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- | 0.2135 | 51.9231 | 135 | 0.4878 | 0.8059 |
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- | 0.2135 | 52.6923 | 137 | 0.8446 | 0.6813 |
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- | 0.1917 | 53.8462 | 140 | 0.5000 | 0.7985 |
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- | 0.1917 | 55.0 | 143 | 0.6128 | 0.7436 |
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- | 0.1917 | 55.7692 | 145 | 0.4510 | 0.8205 |
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- | 0.1917 | 56.9231 | 148 | 0.7576 | 0.6886 |
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- | 0.19 | 57.6923 | 150 | 0.7117 | 0.7106 |
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- | 0.19 | 58.8462 | 153 | 0.5544 | 0.7692 |
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- | 0.19 | 60.0 | 156 | 0.6071 | 0.7363 |
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- | 0.19 | 60.7692 | 158 | 0.6992 | 0.6813 |
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- | 0.1694 | 61.9231 | 161 | 0.6138 | 0.7436 |
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- | 0.1694 | 62.6923 | 163 | 0.4769 | 0.8205 |
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- | 0.1694 | 63.8462 | 166 | 0.5621 | 0.7729 |
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- | 0.1694 | 65.0 | 169 | 0.6584 | 0.7289 |
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- | 0.1675 | 65.7692 | 171 | 0.6404 | 0.7509 |
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- | 0.1675 | 66.9231 | 174 | 0.7474 | 0.7106 |
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- | 0.1675 | 67.6923 | 176 | 0.7257 | 0.7143 |
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- | 0.1675 | 68.8462 | 179 | 0.5354 | 0.7949 |
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- | 0.1673 | 70.0 | 182 | 0.6147 | 0.7582 |
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- | 0.1673 | 70.7692 | 184 | 0.5296 | 0.7875 |
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- | 0.1673 | 71.9231 | 187 | 0.4454 | 0.8168 |
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- | 0.1673 | 72.6923 | 189 | 0.5037 | 0.7949 |
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- | 0.1453 | 73.8462 | 192 | 0.5787 | 0.7656 |
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- | 0.1453 | 75.0 | 195 | 0.6320 | 0.7546 |
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- | 0.1453 | 75.7692 | 197 | 0.6137 | 0.7619 |
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- | 0.1313 | 76.9231 | 200 | 0.6039 | 0.7619 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
23
  metrics:
24
  - name: Accuracy
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  type: accuracy
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+ value: 0.7435897435897436
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  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
33
 
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  This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5816
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+ - Accuracy: 0.7436
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 256
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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: 0.2
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  - num_epochs: 100
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+ - label_smoothing_factor: 0.1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 0.9231 | 3 | 0.4160 | 0.8791 |
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+ | No log | 1.8462 | 6 | 0.4668 | 0.8388 |
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+ | No log | 2.7692 | 9 | 0.5433 | 0.8022 |
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+ | 0.3869 | 4.0 | 13 | 0.5052 | 0.8168 |
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+ | 0.3869 | 4.9231 | 16 | 0.4591 | 0.8571 |
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+ | 0.3869 | 5.8462 | 19 | 0.4820 | 0.8278 |
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+ | 0.3658 | 6.7692 | 22 | 0.4953 | 0.8095 |
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+ | 0.3658 | 8.0 | 26 | 0.4497 | 0.8608 |
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+ | 0.3658 | 8.9231 | 29 | 0.4686 | 0.8315 |
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+ | 0.3439 | 9.8462 | 32 | 0.4506 | 0.8608 |
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+ | 0.3439 | 10.7692 | 35 | 0.4859 | 0.8168 |
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+ | 0.3439 | 12.0 | 39 | 0.4929 | 0.8168 |
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+ | 0.3416 | 12.9231 | 42 | 0.4957 | 0.8059 |
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+ | 0.3416 | 13.8462 | 45 | 0.5229 | 0.7875 |
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+ | 0.3416 | 14.7692 | 48 | 0.4473 | 0.8535 |
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+ | 0.324 | 16.0 | 52 | 0.5260 | 0.8059 |
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+ | 0.324 | 16.9231 | 55 | 0.4582 | 0.8462 |
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+ | 0.324 | 17.8462 | 58 | 0.5299 | 0.7839 |
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+ | 0.3273 | 18.7692 | 61 | 0.4947 | 0.8205 |
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+ | 0.3273 | 20.0 | 65 | 0.5393 | 0.7692 |
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+ | 0.3273 | 20.9231 | 68 | 0.4916 | 0.8278 |
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+ | 0.3397 | 21.8462 | 71 | 0.5360 | 0.7802 |
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+ | 0.3397 | 22.7692 | 74 | 0.5661 | 0.7656 |
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+ | 0.3397 | 24.0 | 78 | 0.6354 | 0.7216 |
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+ | 0.3344 | 24.9231 | 81 | 0.6782 | 0.7033 |
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+ | 0.3344 | 25.8462 | 84 | 0.5704 | 0.7582 |
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+ | 0.3344 | 26.7692 | 87 | 0.6537 | 0.6777 |
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+ | 0.3325 | 28.0 | 91 | 0.4798 | 0.8425 |
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+ | 0.3325 | 28.9231 | 94 | 0.5158 | 0.8059 |
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+ | 0.3325 | 29.8462 | 97 | 0.5408 | 0.7912 |
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+ | 0.3283 | 30.7692 | 100 | 0.5964 | 0.7399 |
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+ | 0.3283 | 32.0 | 104 | 0.5069 | 0.8205 |
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+ | 0.3283 | 32.9231 | 107 | 0.5396 | 0.7875 |
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+ | 0.3229 | 33.8462 | 110 | 0.5203 | 0.7985 |
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+ | 0.3229 | 34.7692 | 113 | 0.5464 | 0.7875 |
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+ | 0.3229 | 36.0 | 117 | 0.5890 | 0.7509 |
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+ | 0.3207 | 36.9231 | 120 | 0.5080 | 0.8132 |
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+ | 0.3207 | 37.8462 | 123 | 0.4944 | 0.8168 |
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+ | 0.3207 | 38.7692 | 126 | 0.4968 | 0.8095 |
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+ | 0.3286 | 40.0 | 130 | 0.4874 | 0.8132 |
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+ | 0.3286 | 40.9231 | 133 | 0.5013 | 0.8059 |
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+ | 0.3286 | 41.8462 | 136 | 0.5329 | 0.7656 |
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+ | 0.3286 | 42.7692 | 139 | 0.6199 | 0.6996 |
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+ | 0.3154 | 44.0 | 143 | 0.4854 | 0.8059 |
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+ | 0.3154 | 44.9231 | 146 | 0.5545 | 0.7509 |
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+ | 0.3154 | 45.8462 | 149 | 0.5267 | 0.7729 |
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+ | 0.3119 | 46.7692 | 152 | 0.5214 | 0.7802 |
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+ | 0.3119 | 48.0 | 156 | 0.5265 | 0.7839 |
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+ | 0.3119 | 48.9231 | 159 | 0.5137 | 0.7985 |
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+ | 0.3036 | 49.8462 | 162 | 0.5354 | 0.7839 |
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+ | 0.3036 | 50.7692 | 165 | 0.5269 | 0.7875 |
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+ | 0.3036 | 52.0 | 169 | 0.5797 | 0.7399 |
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+ | 0.2995 | 52.9231 | 172 | 0.6258 | 0.7179 |
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+ | 0.2995 | 53.8462 | 175 | 0.5512 | 0.7692 |
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+ | 0.2995 | 54.7692 | 178 | 0.5517 | 0.7619 |
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+ | 0.306 | 56.0 | 182 | 0.5590 | 0.7546 |
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+ | 0.306 | 56.9231 | 185 | 0.5514 | 0.7619 |
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+ | 0.306 | 57.8462 | 188 | 0.5597 | 0.7509 |
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+ | 0.2989 | 58.7692 | 191 | 0.5957 | 0.7326 |
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+ | 0.2989 | 60.0 | 195 | 0.5366 | 0.7766 |
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+ | 0.2989 | 60.9231 | 198 | 0.5465 | 0.7729 |
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+ | 0.2931 | 61.8462 | 201 | 0.6171 | 0.7253 |
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+ | 0.2931 | 62.7692 | 204 | 0.5768 | 0.7509 |
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+ | 0.2931 | 64.0 | 208 | 0.5706 | 0.7509 |
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+ | 0.299 | 64.9231 | 211 | 0.5962 | 0.7363 |
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+ | 0.299 | 65.8462 | 214 | 0.6220 | 0.7216 |
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+ | 0.299 | 66.7692 | 217 | 0.5929 | 0.7363 |
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+ | 0.2969 | 68.0 | 221 | 0.6136 | 0.7253 |
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+ | 0.2969 | 68.9231 | 224 | 0.6092 | 0.7289 |
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+ | 0.2969 | 69.8462 | 227 | 0.6029 | 0.7253 |
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+ | 0.3015 | 70.7692 | 230 | 0.5356 | 0.7766 |
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+ | 0.3015 | 72.0 | 234 | 0.5376 | 0.7692 |
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+ | 0.3015 | 72.9231 | 237 | 0.5886 | 0.7436 |
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+ | 0.2919 | 73.8462 | 240 | 0.5869 | 0.7436 |
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+ | 0.2919 | 74.7692 | 243 | 0.5846 | 0.7473 |
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+ | 0.2919 | 76.0 | 247 | 0.5507 | 0.7656 |
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+ | 0.288 | 76.9231 | 250 | 0.5801 | 0.7509 |
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+ | 0.288 | 77.8462 | 253 | 0.6077 | 0.7399 |
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+ | 0.288 | 78.7692 | 256 | 0.5848 | 0.7436 |
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+ | 0.2951 | 80.0 | 260 | 0.5435 | 0.7692 |
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+ | 0.2951 | 80.9231 | 263 | 0.5638 | 0.7656 |
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+ | 0.2951 | 81.8462 | 266 | 0.5795 | 0.7399 |
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+ | 0.2951 | 82.7692 | 269 | 0.5774 | 0.7509 |
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+ | 0.2875 | 84.0 | 273 | 0.5703 | 0.7509 |
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+ | 0.2875 | 84.9231 | 276 | 0.5713 | 0.7509 |
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+ | 0.2875 | 85.8462 | 279 | 0.5784 | 0.7473 |
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+ | 0.2855 | 86.7692 | 282 | 0.5904 | 0.7436 |
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+ | 0.2855 | 88.0 | 286 | 0.5917 | 0.7326 |
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+ | 0.2855 | 88.9231 | 289 | 0.5860 | 0.7473 |
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+ | 0.2964 | 89.8462 | 292 | 0.5858 | 0.7473 |
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+ | 0.2964 | 90.7692 | 295 | 0.5823 | 0.7436 |
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+ | 0.2964 | 92.0 | 299 | 0.5817 | 0.7436 |
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+ | 0.291 | 92.3077 | 300 | 0.5816 | 0.7436 |
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  ### Framework versions
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