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hkivancoral
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smids_1x_beit_base_rms_0001_fold2

Image Classification
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beit
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End of training

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  1. README.md +54 -54
  2. model.safetensors +1 -1
  3. runs/Dec03_16-25-56_e7698b4f2488/events.out.tfevents.1701620757.e7698b4f2488.1907.1 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8219633943427621
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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
@@ -32,8 +32,8 @@ 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: 1.6577
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- - Accuracy: 0.8220
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  ## Model description
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@@ -52,7 +52,7 @@ 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: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -65,56 +65,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.2405 | 1.0 | 75 | 1.1807 | 0.3344 |
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- | 0.9469 | 2.0 | 150 | 0.8999 | 0.5774 |
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- | 0.8786 | 3.0 | 225 | 0.8513 | 0.5258 |
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- | 0.9243 | 4.0 | 300 | 0.7321 | 0.6140 |
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- | 0.7952 | 5.0 | 375 | 0.6848 | 0.6872 |
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- | 0.6242 | 6.0 | 450 | 0.7697 | 0.6789 |
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- | 0.7242 | 7.0 | 525 | 0.5894 | 0.7537 |
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- | 0.6 | 8.0 | 600 | 0.5578 | 0.7488 |
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- | 0.5406 | 9.0 | 675 | 0.5698 | 0.7820 |
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- | 0.5792 | 10.0 | 750 | 0.5606 | 0.7571 |
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- | 0.4505 | 11.0 | 825 | 0.5453 | 0.7820 |
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- | 0.3414 | 12.0 | 900 | 0.5924 | 0.8087 |
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- | 0.3573 | 13.0 | 975 | 0.5677 | 0.8037 |
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- | 0.2499 | 14.0 | 1050 | 0.6842 | 0.7787 |
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- | 0.2823 | 15.0 | 1125 | 0.8625 | 0.7804 |
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- | 0.3002 | 16.0 | 1200 | 0.6910 | 0.7970 |
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- | 0.2766 | 17.0 | 1275 | 0.6017 | 0.8037 |
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- | 0.1734 | 18.0 | 1350 | 0.7840 | 0.7920 |
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- | 0.1566 | 19.0 | 1425 | 1.0187 | 0.7654 |
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- | 0.1746 | 20.0 | 1500 | 0.8603 | 0.7937 |
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- | 0.1652 | 21.0 | 1575 | 0.8901 | 0.7837 |
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- | 0.0963 | 22.0 | 1650 | 0.9939 | 0.7870 |
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- | 0.0718 | 23.0 | 1725 | 1.0755 | 0.8070 |
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- | 0.0965 | 24.0 | 1800 | 1.0700 | 0.8053 |
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- | 0.085 | 25.0 | 1875 | 1.2743 | 0.7987 |
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- | 0.0366 | 26.0 | 1950 | 1.5329 | 0.7987 |
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- | 0.0583 | 27.0 | 2025 | 1.3699 | 0.7837 |
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- | 0.0317 | 28.0 | 2100 | 1.4532 | 0.8020 |
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- | 0.1066 | 29.0 | 2175 | 1.1729 | 0.8053 |
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- | 0.05 | 30.0 | 2250 | 1.8098 | 0.7820 |
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- | 0.0677 | 31.0 | 2325 | 1.5361 | 0.7953 |
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- | 0.0393 | 32.0 | 2400 | 1.5054 | 0.7787 |
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- | 0.0795 | 33.0 | 2475 | 1.6585 | 0.7937 |
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- | 0.011 | 34.0 | 2550 | 2.0247 | 0.7687 |
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- | 0.0542 | 35.0 | 2625 | 1.8399 | 0.7953 |
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- | 0.0312 | 36.0 | 2700 | 1.5389 | 0.8103 |
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- | 0.0381 | 37.0 | 2775 | 1.7429 | 0.8103 |
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- | 0.0196 | 38.0 | 2850 | 1.7985 | 0.7953 |
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- | 0.0297 | 39.0 | 2925 | 1.6892 | 0.8203 |
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- | 0.0043 | 40.0 | 3000 | 1.5819 | 0.8170 |
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- | 0.0288 | 41.0 | 3075 | 1.6717 | 0.8053 |
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- | 0.0284 | 42.0 | 3150 | 1.6970 | 0.8136 |
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- | 0.0277 | 43.0 | 3225 | 1.7867 | 0.8037 |
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- | 0.0134 | 44.0 | 3300 | 1.8167 | 0.8120 |
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- | 0.0311 | 45.0 | 3375 | 1.6292 | 0.8053 |
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- | 0.018 | 46.0 | 3450 | 1.6267 | 0.8203 |
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- | 0.0078 | 47.0 | 3525 | 1.6457 | 0.8220 |
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- | 0.0075 | 48.0 | 3600 | 1.6501 | 0.8170 |
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- | 0.0049 | 49.0 | 3675 | 1.6557 | 0.8203 |
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- | 0.0087 | 50.0 | 3750 | 1.6577 | 0.8220 |
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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: 0.740432612312812
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  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: 0.9358
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+ - Accuracy: 0.7404
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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: 0.001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.0437 | 1.0 | 75 | 0.9679 | 0.5042 |
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+ | 0.9234 | 2.0 | 150 | 0.8669 | 0.5208 |
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+ | 1.0795 | 3.0 | 225 | 0.7926 | 0.5874 |
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+ | 0.9543 | 4.0 | 300 | 0.8244 | 0.5507 |
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+ | 0.8239 | 5.0 | 375 | 0.7959 | 0.5857 |
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+ | 0.7924 | 6.0 | 450 | 0.7928 | 0.5890 |
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+ | 0.8468 | 7.0 | 525 | 0.7806 | 0.6256 |
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+ | 0.8608 | 8.0 | 600 | 0.9027 | 0.5408 |
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+ | 0.7878 | 9.0 | 675 | 0.7544 | 0.6373 |
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+ | 0.9079 | 10.0 | 750 | 0.7732 | 0.6190 |
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+ | 0.7705 | 11.0 | 825 | 0.7349 | 0.6290 |
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+ | 0.7586 | 12.0 | 900 | 0.7322 | 0.6306 |
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+ | 0.7794 | 13.0 | 975 | 0.7224 | 0.6323 |
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+ | 0.7123 | 14.0 | 1050 | 0.7252 | 0.6572 |
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+ | 0.744 | 15.0 | 1125 | 0.7450 | 0.5990 |
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+ | 0.7086 | 16.0 | 1200 | 0.6962 | 0.6639 |
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+ | 0.7295 | 17.0 | 1275 | 0.7508 | 0.6489 |
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+ | 0.7289 | 18.0 | 1350 | 0.6978 | 0.6722 |
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+ | 0.6947 | 19.0 | 1425 | 0.7112 | 0.6739 |
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+ | 0.6923 | 20.0 | 1500 | 0.7131 | 0.6805 |
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+ | 0.7545 | 21.0 | 1575 | 0.7480 | 0.6223 |
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+ | 0.68 | 22.0 | 1650 | 0.6683 | 0.6839 |
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+ | 0.7107 | 23.0 | 1725 | 0.6889 | 0.6772 |
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+ | 0.6933 | 24.0 | 1800 | 0.6566 | 0.6822 |
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+ | 0.6429 | 25.0 | 1875 | 0.6381 | 0.7005 |
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+ | 0.6742 | 26.0 | 1950 | 0.6536 | 0.6822 |
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+ | 0.6753 | 27.0 | 2025 | 0.6462 | 0.6889 |
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+ | 0.6228 | 28.0 | 2100 | 0.6368 | 0.7022 |
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+ | 0.6193 | 29.0 | 2175 | 0.6115 | 0.7171 |
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+ | 0.5568 | 30.0 | 2250 | 0.6625 | 0.7188 |
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+ | 0.584 | 31.0 | 2325 | 0.6680 | 0.6922 |
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+ | 0.581 | 32.0 | 2400 | 0.5723 | 0.7654 |
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+ | 0.5698 | 33.0 | 2475 | 0.6173 | 0.7205 |
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+ | 0.5032 | 34.0 | 2550 | 0.6176 | 0.7338 |
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+ | 0.5019 | 35.0 | 2625 | 0.6137 | 0.7438 |
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+ | 0.4921 | 36.0 | 2700 | 0.5855 | 0.7571 |
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+ | 0.453 | 37.0 | 2775 | 0.6724 | 0.7271 |
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+ | 0.4913 | 38.0 | 2850 | 0.6043 | 0.7720 |
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+ | 0.3871 | 39.0 | 2925 | 0.6124 | 0.7704 |
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+ | 0.4014 | 40.0 | 3000 | 0.6591 | 0.7521 |
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+ | 0.4698 | 41.0 | 3075 | 0.6575 | 0.7604 |
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+ | 0.375 | 42.0 | 3150 | 0.6735 | 0.7471 |
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+ | 0.317 | 43.0 | 3225 | 0.7867 | 0.7504 |
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+ | 0.2968 | 44.0 | 3300 | 0.7423 | 0.7521 |
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+ | 0.2919 | 45.0 | 3375 | 0.8253 | 0.7504 |
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+ | 0.2598 | 46.0 | 3450 | 0.8629 | 0.7421 |
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+ | 0.1951 | 47.0 | 3525 | 0.8586 | 0.7704 |
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+ | 0.1905 | 48.0 | 3600 | 0.9010 | 0.7438 |
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+ | 0.1278 | 49.0 | 3675 | 0.9354 | 0.7454 |
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+ | 0.2294 | 50.0 | 3750 | 0.9358 | 0.7404 |
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  ### Framework versions
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