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hkivancoral
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smids_1x_beit_base_rms_0001_fold1

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_14-37-47_e7698b4f2488/events.out.tfevents.1701614275.e7698b4f2488.1907.0 +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: 0.8063439065108514
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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: 1.8942
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- - Accuracy: 0.8063
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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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- | 1.13 | 1.0 | 76 | 1.2056 | 0.3356 |
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- | 0.8974 | 2.0 | 152 | 0.8387 | 0.5225 |
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- | 0.8196 | 3.0 | 228 | 1.4651 | 0.3222 |
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- | 0.8985 | 4.0 | 304 | 1.2356 | 0.6294 |
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- | 0.8372 | 5.0 | 380 | 0.7552 | 0.6477 |
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- | 0.7083 | 6.0 | 456 | 0.9199 | 0.5626 |
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- | 0.684 | 7.0 | 532 | 0.6721 | 0.7112 |
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- | 0.6132 | 8.0 | 608 | 0.7876 | 0.6361 |
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- | 0.6026 | 9.0 | 684 | 0.6696 | 0.6995 |
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- | 0.5266 | 10.0 | 760 | 0.9107 | 0.6811 |
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- | 0.5286 | 11.0 | 836 | 0.6819 | 0.7179 |
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- | 0.4686 | 12.0 | 912 | 0.7309 | 0.7078 |
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- | 0.3852 | 13.0 | 988 | 0.7123 | 0.6912 |
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- | 0.3965 | 14.0 | 1064 | 0.6629 | 0.7312 |
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- | 0.3322 | 15.0 | 1140 | 0.7871 | 0.7279 |
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- | 0.3224 | 16.0 | 1216 | 0.6397 | 0.7613 |
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- | 0.2843 | 17.0 | 1292 | 0.6977 | 0.7646 |
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- | 0.2694 | 18.0 | 1368 | 0.7334 | 0.7730 |
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- | 0.1816 | 19.0 | 1444 | 0.8893 | 0.7596 |
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- | 0.2735 | 20.0 | 1520 | 0.8686 | 0.7496 |
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- | 0.2003 | 21.0 | 1596 | 0.8753 | 0.7796 |
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- | 0.1859 | 22.0 | 1672 | 0.9795 | 0.7780 |
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- | 0.084 | 23.0 | 1748 | 1.0051 | 0.7813 |
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- | 0.1582 | 24.0 | 1824 | 1.1377 | 0.7763 |
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- | 0.0427 | 25.0 | 1900 | 1.3501 | 0.7963 |
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- | 0.0902 | 26.0 | 1976 | 1.1910 | 0.8013 |
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- | 0.094 | 27.0 | 2052 | 1.1754 | 0.7846 |
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- | 0.0857 | 28.0 | 2128 | 1.1353 | 0.7629 |
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- | 0.1026 | 29.0 | 2204 | 1.1656 | 0.8013 |
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- | 0.0352 | 30.0 | 2280 | 1.5812 | 0.7679 |
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- | 0.0669 | 31.0 | 2356 | 1.2748 | 0.7913 |
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- | 0.0774 | 32.0 | 2432 | 1.4963 | 0.7730 |
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- | 0.0572 | 33.0 | 2508 | 1.4554 | 0.7930 |
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- | 0.0581 | 34.0 | 2584 | 1.4560 | 0.7913 |
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- | 0.111 | 35.0 | 2660 | 1.5007 | 0.7679 |
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- | 0.0709 | 36.0 | 2736 | 1.5723 | 0.7930 |
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- | 0.0602 | 37.0 | 2812 | 1.6194 | 0.7880 |
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- | 0.0108 | 38.0 | 2888 | 1.8313 | 0.7863 |
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- | 0.0234 | 39.0 | 2964 | 1.6779 | 0.7997 |
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- | 0.0137 | 40.0 | 3040 | 1.9642 | 0.7896 |
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- | 0.0137 | 41.0 | 3116 | 2.0493 | 0.7746 |
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- | 0.0012 | 42.0 | 3192 | 1.8332 | 0.8080 |
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- | 0.0355 | 43.0 | 3268 | 1.7792 | 0.7930 |
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- | 0.0 | 44.0 | 3344 | 1.9306 | 0.7947 |
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- | 0.0066 | 45.0 | 3420 | 1.8384 | 0.7980 |
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- | 0.0002 | 46.0 | 3496 | 1.8649 | 0.7997 |
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- | 0.0117 | 47.0 | 3572 | 1.8524 | 0.8097 |
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- | 0.017 | 48.0 | 3648 | 1.9232 | 0.8047 |
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- | 0.0046 | 49.0 | 3724 | 1.8951 | 0.8063 |
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- | 0.0009 | 50.0 | 3800 | 1.8942 | 0.8063 |
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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: 0.6978297161936561
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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
 
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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.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.7464
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+ - Accuracy: 0.6978
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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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+ | 1.1002 | 1.0 | 76 | 0.9320 | 0.5459 |
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+ | 0.9176 | 2.0 | 152 | 0.9156 | 0.4975 |
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+ | 0.8828 | 3.0 | 228 | 1.4808 | 0.3239 |
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+ | 0.9116 | 4.0 | 304 | 0.9182 | 0.5058 |
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+ | 0.9681 | 5.0 | 380 | 0.8261 | 0.5726 |
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+ | 0.8914 | 6.0 | 456 | 0.8412 | 0.5442 |
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+ | 0.8118 | 7.0 | 532 | 0.8070 | 0.5843 |
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+ | 0.7886 | 8.0 | 608 | 0.7873 | 0.6144 |
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+ | 0.8228 | 9.0 | 684 | 0.8018 | 0.5593 |
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+ | 0.7855 | 10.0 | 760 | 0.8650 | 0.5659 |
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+ | 0.7506 | 11.0 | 836 | 0.8105 | 0.5726 |
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+ | 0.8105 | 12.0 | 912 | 0.7718 | 0.5760 |
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+ | 0.7542 | 13.0 | 988 | 0.7814 | 0.6027 |
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+ | 0.8063 | 14.0 | 1064 | 0.7598 | 0.6244 |
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+ | 0.6853 | 15.0 | 1140 | 0.9554 | 0.5526 |
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+ | 0.6995 | 16.0 | 1216 | 0.7869 | 0.6277 |
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+ | 0.7413 | 17.0 | 1292 | 0.7345 | 0.6561 |
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+ | 0.6942 | 18.0 | 1368 | 0.7274 | 0.6511 |
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+ | 0.7698 | 19.0 | 1444 | 0.7431 | 0.6711 |
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+ | 0.7328 | 20.0 | 1520 | 0.7361 | 0.6327 |
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+ | 0.7002 | 21.0 | 1596 | 0.7435 | 0.6427 |
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+ | 0.6967 | 22.0 | 1672 | 0.8269 | 0.6010 |
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+ | 0.651 | 23.0 | 1748 | 0.7688 | 0.6528 |
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+ | 0.6937 | 24.0 | 1824 | 0.7386 | 0.6578 |
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+ | 0.5694 | 25.0 | 1900 | 0.7657 | 0.6277 |
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+ | 0.6705 | 26.0 | 1976 | 0.7210 | 0.6811 |
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+ | 0.5989 | 27.0 | 2052 | 0.7453 | 0.6561 |
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+ | 0.6274 | 28.0 | 2128 | 0.7780 | 0.6578 |
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+ | 0.5748 | 29.0 | 2204 | 0.7338 | 0.6845 |
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+ | 0.6764 | 30.0 | 2280 | 0.7373 | 0.6394 |
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+ | 0.6934 | 31.0 | 2356 | 0.7055 | 0.6845 |
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+ | 0.6007 | 32.0 | 2432 | 0.7394 | 0.6511 |
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+ | 0.5933 | 33.0 | 2508 | 0.7124 | 0.6795 |
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+ | 0.5894 | 34.0 | 2584 | 0.7760 | 0.6711 |
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+ | 0.6837 | 35.0 | 2660 | 0.7002 | 0.6628 |
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+ | 0.5776 | 36.0 | 2736 | 0.7352 | 0.6694 |
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+ | 0.6485 | 37.0 | 2812 | 0.7046 | 0.6878 |
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+ | 0.5352 | 38.0 | 2888 | 0.7058 | 0.6861 |
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+ | 0.577 | 39.0 | 2964 | 0.6974 | 0.7028 |
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+ | 0.5712 | 40.0 | 3040 | 0.7122 | 0.6811 |
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+ | 0.5117 | 41.0 | 3116 | 0.7026 | 0.6845 |
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+ | 0.4908 | 42.0 | 3192 | 0.7187 | 0.7045 |
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+ | 0.4784 | 43.0 | 3268 | 0.7103 | 0.7028 |
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+ | 0.4739 | 44.0 | 3344 | 0.7027 | 0.7162 |
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+ | 0.5942 | 45.0 | 3420 | 0.7242 | 0.6962 |
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+ | 0.4258 | 46.0 | 3496 | 0.7593 | 0.6912 |
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+ | 0.4726 | 47.0 | 3572 | 0.7433 | 0.6895 |
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+ | 0.4422 | 48.0 | 3648 | 0.7412 | 0.6928 |
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+ | 0.4049 | 49.0 | 3724 | 0.7425 | 0.6995 |
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+ | 0.5059 | 50.0 | 3800 | 0.7464 | 0.6978 |
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  ### Framework versions
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