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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-1k-224 |
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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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- precision |
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model-index: |
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- name: convnextv2-tiny-1k-224-finetuned-crop-neck-style |
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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: 0.8127853881278538 |
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- name: Precision |
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type: precision |
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value: 0.8388735739429154 |
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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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# convnextv2-tiny-1k-224-finetuned-crop-neck-style |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7591 |
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- Accuracy: 0.8128 |
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- Precision: 0.8389 |
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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: 2e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 4 |
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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: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:| |
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| No log | 1.0 | 88 | 0.7865 | 0.7717 | 0.8100 | |
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| No log | 2.0 | 176 | 0.7591 | 0.8128 | 0.8389 | |
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| No log | 3.0 | 264 | 0.8601 | 0.7854 | 0.8206 | |
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| No log | 4.0 | 352 | 0.8518 | 0.8082 | 0.8454 | |
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| No log | 5.0 | 440 | 0.7959 | 0.8128 | 0.8295 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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