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vit-base-patch16-224-ve-U13b-80RX1

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  1. README.md +47 -45
  2. model.safetensors +1 -1
  3. runs/Jun23_15-37-09_DESKTOP-SKBE9FB/events.out.tfevents.1719178630.DESKTOP-SKBE9FB.2132.0 +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: 0.10869565217391304
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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 [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: 25872499347325405328572416.0000
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- - Accuracy: 0.1087
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  ## Model description
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@@ -56,6 +56,8 @@ The following hyperparameters were used during training:
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
 
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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: 0.05
@@ -63,48 +65,48 @@ The following hyperparameters were used during training:
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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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- | 21407918734188223332876288.0000 | 1.0 | 103 | 25872499347325405328572416.0000 | 0.1087 |
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- | 19230841377306649816989696.0000 | 2.0 | 206 | 25872499347325405328572416.0000 | 0.1087 |
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- | 22859301179210058793222144.0000 | 3.0 | 309 | 25872499347325405328572416.0000 | 0.1087 |
71
- | 23584992401720978670878720.0000 | 4.0 | 412 | 25872499347325405328572416.0000 | 0.1087 |
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- | 24310687313580712431452160.0000 | 5.0 | 515 | 25872499347325405328572416.0000 | 0.1087 |
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- | 24310687313580712431452160.0000 | 6.0 | 618 | 25872499347325405328572416.0000 | 0.1087 |
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- | 22496457412629005795852288.0000 | 7.0 | 721 | 25872499347325405328572416.0000 | 0.1087 |
75
- | 21045071278258356452589568.0000 | 8.0 | 824 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21045071278258356452589568.0000 | 9.0 | 927 | 25872499347325405328572416.0000 | 0.1087 |
77
- | 23343098402008016231596032.0000 | 10.0 | 1030 | 25872499347325405328572416.0000 | 0.1087 |
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- | 23222148635139925673508864.0000 | 11.0 | 1133 | 25872499347325405328572416.0000 | 0.1087 |
79
- | 23222150479814334762450944.0000 | 12.0 | 1236 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21407918734188223332876288.0000 | 13.0 | 1339 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21407916889513814243934208.0000 | 14.0 | 1442 | 25872499347325405328572416.0000 | 0.1087 |
82
- | 21770764345443681124220928.0000 | 15.0 | 1545 | 25872499347325405328572416.0000 | 0.1087 |
83
- | 22496455567954601001877504.0000 | 16.0 | 1648 | 25872499347325405328572416.0000 | 0.1087 |
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- | 22859303023884467882164224.0000 | 17.0 | 1751 | 25872499347325405328572416.0000 | 0.1087 |
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- | 19593686988562107608334336.0000 | 18.0 | 1854 | 25872499347325405328572416.0000 | 0.1087 |
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- | 22859304868558872676139008.0000 | 19.0 | 1957 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21528866656381904802021376.0000 | 20.0 | 2060 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21407918734188223332876288.0000 | 29.0 | 2987 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21407920578862628126851072.0000 | 35.0 | 3605 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21770764345443681124220928.0000 | 37.0 | 3811 | 25872499347325405328572416.0000 | 0.1087 |
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- | 22496457412629005795852288.0000 | 38.0 | 3914 | 25872499347325405328572416.0000 | 0.1087 |
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- | 21407918734188223332876288.0000 | 39.0 | 4017 | 25872499347325405328572416.0000 | 0.1087 |
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- | 23222148635139925673508864.0000 | 40.0 | 4120 | 25872499347325405328572416.0000 | 0.1087 |
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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: 0.717391304347826
26
  ---
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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 25847261397527471458109882368.0000
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+ - Accuracy: 0.7174
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  ## Model description
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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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: 0.05
 
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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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+ | 62421131753362939742087282688.0000 | 0.99 | 51 | 25847261397527471458109882368.0000 | 0.3696 |
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+ | 68366009359745128248184406016.0000 | 2.0 | 103 | 25847261397527471458109882368.0000 | 0.5435 |
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+ | 56476265480660303086264254464.0000 | 2.99 | 154 | 25847261397527471458109882368.0000 | 0.7174 |
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  ### Framework versions
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