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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-upperfull-mix |
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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: 0.8004385964912281 |
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- name: Precision |
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type: precision |
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value: 0.8160100686256399 |
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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-upperfull-mix |
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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: 0.6187 |
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- Accuracy: 0.8004 |
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- Precision: 0.8160 |
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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 | 183 | 2.1471 | 0.4693 | 0.5128 | |
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| No log | 2.0 | 366 | 1.4576 | 0.6579 | 0.6955 | |
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| 1.9821 | 3.0 | 549 | 1.1372 | 0.6754 | 0.7183 | |
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| 1.9821 | 4.0 | 732 | 0.9214 | 0.7303 | 0.7659 | |
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| 1.9821 | 5.0 | 915 | 0.7792 | 0.7478 | 0.7661 | |
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| 0.8885 | 6.0 | 1098 | 0.7455 | 0.7654 | 0.7780 | |
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| 0.8885 | 7.0 | 1281 | 0.6756 | 0.7873 | 0.8020 | |
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| 0.8885 | 8.0 | 1464 | 0.6787 | 0.7807 | 0.7932 | |
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| 0.5696 | 9.0 | 1647 | 0.6694 | 0.7982 | 0.8099 | |
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| 0.5696 | 10.0 | 1830 | 0.6799 | 0.7741 | 0.7930 | |
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| 0.4056 | 11.0 | 2013 | 0.6187 | 0.8004 | 0.8160 | |
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| 0.4056 | 12.0 | 2196 | 0.6868 | 0.7675 | 0.8063 | |
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| 0.4056 | 13.0 | 2379 | 0.7525 | 0.7544 | 0.7803 | |
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| 0.2904 | 14.0 | 2562 | 0.6572 | 0.7895 | 0.8093 | |
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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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