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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-neckline |
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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: 0.8095238095238095 |
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- name: Precision |
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type: precision |
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value: 0.8100590473699718 |
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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-neckline |
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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: 0.6160 |
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- Accuracy: 0.8095 |
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- Precision: 0.8101 |
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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 | 84 | 1.4226 | 0.5619 | 0.5870 | |
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| No log | 2.0 | 168 | 1.1924 | 0.5619 | 0.5663 | |
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| No log | 3.0 | 252 | 0.9542 | 0.6952 | 0.7317 | |
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| No log | 4.0 | 336 | 0.8255 | 0.7143 | 0.7224 | |
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| No log | 5.0 | 420 | 0.7614 | 0.7190 | 0.7378 | |
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| 1.1937 | 6.0 | 504 | 0.7303 | 0.7381 | 0.7454 | |
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| 1.1937 | 7.0 | 588 | 0.6770 | 0.7667 | 0.7772 | |
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| 1.1937 | 8.0 | 672 | 0.6849 | 0.7667 | 0.7748 | |
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| 1.1937 | 9.0 | 756 | 0.6720 | 0.7381 | 0.7532 | |
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| 1.1937 | 10.0 | 840 | 0.7036 | 0.7286 | 0.7429 | |
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| 1.1937 | 11.0 | 924 | 0.6752 | 0.7619 | 0.7827 | |
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| 0.6846 | 12.0 | 1008 | 0.6399 | 0.7810 | 0.7860 | |
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| 0.6846 | 13.0 | 1092 | 0.6860 | 0.7381 | 0.7553 | |
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| 0.6846 | 14.0 | 1176 | 0.6827 | 0.7476 | 0.7644 | |
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| 0.6846 | 15.0 | 1260 | 0.6160 | 0.8095 | 0.8101 | |
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| 0.6846 | 16.0 | 1344 | 0.7032 | 0.7619 | 0.7695 | |
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| 0.6846 | 17.0 | 1428 | 0.6916 | 0.8048 | 0.8197 | |
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| 0.5051 | 18.0 | 1512 | 0.7070 | 0.7810 | 0.7891 | |
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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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