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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-bottomCleanedData
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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: 0.9977298524404086
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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-bottomCleanedData
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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: 0.0067
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- Accuracy: 0.9977
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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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| 0.2003 | 1.0 | 141 | 0.0628 | 0.9807 |
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| 0.1568 | 2.0 | 283 | 0.0173 | 0.9943 |
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| 0.1499 | 2.99 | 424 | 0.0211 | 0.9898 |
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| 0.1189 | 4.0 | 566 | 0.0140 | 0.9955 |
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| 0.084 | 4.99 | 707 | 0.0105 | 0.9955 |
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| 0.0797 | 6.0 | 849 | 0.0093 | 0.9966 |
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| 0.0781 | 7.0 | 991 | 0.0157 | 0.9921 |
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| 0.1075 | 8.0 | 1132 | 0.0079 | 0.9943 |
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| 0.0718 | 9.0 | 1274 | 0.0075 | 0.9966 |
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| 0.0592 | 9.96 | 1410 | 0.0067 | 0.9977 |
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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 2.12.0
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- Tokenizers 0.13.3
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