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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: convnext-large-224-22k-1k-convnext_bottom
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9981447124304267
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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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should probably proofread and complete it, then remove this comment. -->
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# convnext-large-224-22k-1k-convnext_bottom
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This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0064
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- Accuracy: 0.9981
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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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: 7
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- total_train_batch_size: 56
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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: 0.01
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- num_epochs: 10
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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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| 0.0899 | 0.99 | 86 | 0.0290 | 0.9852 |
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| 0.0651 | 2.0 | 173 | 0.0217 | 0.9889 |
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| 0.0364 | 2.99 | 259 | 0.0170 | 0.9944 |
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| 0.0678 | 4.0 | 346 | 0.0135 | 0.9963 |
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| 0.0129 | 4.99 | 432 | 0.0120 | 0.9944 |
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| 0.0189 | 6.0 | 519 | 0.0095 | 0.9944 |
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| 0.0399 | 7.0 | 606 | 0.0098 | 0.9944 |
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| 0.029 | 7.99 | 692 | 0.0121 | 0.9963 |
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| 0.0153 | 9.0 | 779 | 0.0068 | 0.9981 |
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| 0.0252 | 9.93 | 860 | 0.0064 | 0.9981 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 2.0.0+cu118
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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