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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-nano-22k-384 |
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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-nano-3e-4-augment |
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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: validation |
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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: 0.9279761904761905 |
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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-nano-3e-4-augment |
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This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2872 |
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- Accuracy: 0.9280 |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: cosine |
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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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| 0.8119 | 1.0 | 275 | 0.5637 | 0.8270 | |
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| 0.5829 | 2.0 | 550 | 0.5016 | 0.8497 | |
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| 0.4623 | 3.0 | 825 | 0.4494 | 0.8755 | |
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| 0.359 | 4.0 | 1100 | 0.3809 | 0.8887 | |
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| 0.2881 | 5.0 | 1375 | 0.3742 | 0.8998 | |
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| 0.2302 | 6.0 | 1650 | 0.3402 | 0.9113 | |
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| 0.1827 | 7.0 | 1925 | 0.3150 | 0.9121 | |
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| 0.1466 | 8.0 | 2200 | 0.3012 | 0.9229 | |
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| 0.1223 | 9.0 | 2475 | 0.2996 | 0.9249 | |
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| 0.1332 | 10.0 | 2750 | 0.2948 | 0.9249 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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