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vuongnhathien
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convnext-base-15ep

Image Classification
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convnextv2
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  1. README.md +19 -19
  2. 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: 0.942063492063492
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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 [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3165
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- - Accuracy: 0.9421
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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: 0.0001
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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- | 0.5717 | 1.0 | 1099 | 0.4616 | 0.8573 |
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- | 0.4653 | 2.0 | 2198 | 0.3607 | 0.8970 |
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- | 0.3449 | 3.0 | 3297 | 0.4104 | 0.8950 |
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- | 0.3522 | 4.0 | 4396 | 0.3755 | 0.9026 |
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- | 0.28 | 5.0 | 5495 | 0.3756 | 0.9066 |
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- | 0.2456 | 6.0 | 6594 | 0.3496 | 0.9173 |
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- | 0.2141 | 7.0 | 7693 | 0.3612 | 0.9201 |
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- | 0.1458 | 8.0 | 8792 | 0.3391 | 0.9304 |
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- | 0.1842 | 9.0 | 9891 | 0.3353 | 0.9328 |
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- | 0.1037 | 10.0 | 10990 | 0.3383 | 0.9356 |
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- | 0.0747 | 11.0 | 12089 | 0.3345 | 0.9368 |
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- | 0.0912 | 12.0 | 13188 | 0.3244 | 0.9392 |
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- | 0.0733 | 13.0 | 14287 | 0.3219 | 0.9408 |
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- | 0.0667 | 14.0 | 15386 | 0.3190 | 0.9435 |
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- | 0.0694 | 15.0 | 16485 | 0.3192 | 0.9431 |
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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: 0.9499005964214712
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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 [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2384
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+ - Accuracy: 0.9499
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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: 3e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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+ | 0.6099 | 1.0 | 1099 | 0.3668 | 0.8934 |
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+ | 0.5086 | 2.0 | 2198 | 0.2773 | 0.9276 |
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+ | 0.386 | 3.0 | 3297 | 0.2587 | 0.9324 |
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+ | 0.335 | 4.0 | 4396 | 0.2400 | 0.9348 |
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+ | 0.3167 | 5.0 | 5495 | 0.2599 | 0.9340 |
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+ | 0.2703 | 6.0 | 6594 | 0.2440 | 0.9419 |
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+ | 0.2638 | 7.0 | 7693 | 0.2496 | 0.9408 |
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+ | 0.1938 | 8.0 | 8792 | 0.2366 | 0.9431 |
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+ | 0.1789 | 9.0 | 9891 | 0.2353 | 0.9487 |
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+ | 0.1738 | 10.0 | 10990 | 0.2380 | 0.9499 |
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+ | 0.1924 | 11.0 | 12089 | 0.2458 | 0.9463 |
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+ | 0.1628 | 12.0 | 13188 | 0.2434 | 0.9491 |
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+ | 0.1431 | 13.0 | 14287 | 0.2390 | 0.9499 |
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+ | 0.1432 | 14.0 | 15386 | 0.2391 | 0.9503 |
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+ | 0.1297 | 15.0 | 16485 | 0.2384 | 0.9499 |
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  ### Framework versions
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