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th041
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vit-weldclassifyv4

Image Classification
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vit
Generated from Trainer
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  1. README.md +23 -23
  2. data/events.out.tfevents.1719216875.545075d08898.349.0 +2 -2
  3. model.safetensors +1 -1
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: 0.8920863309352518
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5885
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- - Accuracy: 0.8921
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  ## Model description
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@@ -65,26 +65,26 @@ 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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- | 1.1598 | 0.6410 | 100 | 1.2082 | 0.4245 |
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- | 0.8688 | 1.2821 | 200 | 1.0834 | 0.5036 |
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- | 0.7211 | 1.9231 | 300 | 0.5981 | 0.7662 |
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- | 0.8013 | 2.5641 | 400 | 1.0970 | 0.5719 |
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- | 0.2789 | 3.2051 | 500 | 0.4212 | 0.8489 |
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- | 0.2397 | 3.8462 | 600 | 0.3963 | 0.8741 |
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- | 0.1034 | 4.4872 | 700 | 0.4437 | 0.8741 |
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- | 0.0448 | 5.1282 | 800 | 0.4933 | 0.8597 |
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- | 0.0473 | 5.7692 | 900 | 0.4663 | 0.8741 |
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- | 0.074 | 6.4103 | 1000 | 0.6667 | 0.8453 |
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- | 0.0488 | 7.0513 | 1100 | 0.5352 | 0.8885 |
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- | 0.0486 | 7.6923 | 1200 | 0.5648 | 0.8885 |
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- | 0.0045 | 8.3333 | 1300 | 0.5992 | 0.8885 |
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- | 0.0912 | 8.9744 | 1400 | 0.5750 | 0.8705 |
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- | 0.0332 | 9.6154 | 1500 | 0.5407 | 0.9029 |
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- | 0.005 | 10.2564 | 1600 | 0.5689 | 0.8885 |
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- | 0.0028 | 10.8974 | 1700 | 0.5718 | 0.8921 |
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- | 0.0027 | 11.5385 | 1800 | 0.5849 | 0.8957 |
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- | 0.0025 | 12.1795 | 1900 | 0.5870 | 0.8921 |
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- | 0.0024 | 12.8205 | 2000 | 0.5885 | 0.8921 |
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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: 0.8884892086330936
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6399
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+ - Accuracy: 0.8885
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 1.1126 | 0.6410 | 100 | 1.0171 | 0.5504 |
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+ | 0.8229 | 1.2821 | 200 | 0.7307 | 0.6942 |
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+ | 0.7224 | 1.9231 | 300 | 0.6399 | 0.7122 |
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+ | 0.3909 | 2.5641 | 400 | 0.5400 | 0.7734 |
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+ | 0.237 | 3.2051 | 500 | 0.6716 | 0.7626 |
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+ | 0.4056 | 3.8462 | 600 | 0.5265 | 0.8094 |
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+ | 0.1764 | 4.4872 | 700 | 0.9174 | 0.7446 |
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+ | 0.0546 | 5.1282 | 800 | 0.6644 | 0.8237 |
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+ | 0.0436 | 5.7692 | 900 | 0.6923 | 0.8345 |
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+ | 0.0661 | 6.4103 | 1000 | 0.6784 | 0.8345 |
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+ | 0.0167 | 7.0513 | 1100 | 0.7115 | 0.8309 |
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+ | 0.0744 | 7.6923 | 1200 | 0.6341 | 0.8525 |
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+ | 0.0047 | 8.3333 | 1300 | 0.6402 | 0.8597 |
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+ | 0.0039 | 8.9744 | 1400 | 0.5958 | 0.8849 |
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+ | 0.0029 | 9.6154 | 1500 | 0.6158 | 0.8885 |
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+ | 0.0027 | 10.2564 | 1600 | 0.6189 | 0.8885 |
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+ | 0.0025 | 10.8974 | 1700 | 0.6309 | 0.8885 |
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+ | 0.0024 | 11.5385 | 1800 | 0.6356 | 0.8885 |
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+ | 0.0023 | 12.1795 | 1900 | 0.6382 | 0.8885 |
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+ | 0.0023 | 12.8205 | 2000 | 0.6399 | 0.8885 |
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  ### Framework versions
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