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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- preprocessed1024_config |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: convnext-mlo-512-breat_composition |
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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: preprocessed1024_config |
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type: preprocessed1024_config |
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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: |
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accuracy: 0.5785175879396985 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.565251065728165 |
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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-mlo-512-breat_composition |
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This model is a fine-tuned version of [](https://huggingface.co./) on the preprocessed1024_config dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1521 |
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- Accuracy: {'accuracy': 0.5785175879396985} |
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- F1: {'f1': 0.565251065728165} |
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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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- 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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:| |
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| 1.3433 | 1.0 | 796 | 1.1893 | {'accuracy': 0.4566582914572864} | {'f1': 0.32080438921262083} | |
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| 1.1242 | 2.0 | 1592 | 1.0867 | {'accuracy': 0.48555276381909546} | {'f1': 0.4061780745199038} | |
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| 1.0569 | 3.0 | 2388 | 1.1587 | {'accuracy': 0.49120603015075376} | {'f1': 0.40970823779940124} | |
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| 0.9327 | 4.0 | 3184 | 0.9901 | {'accuracy': 0.5452261306532663} | {'f1': 0.4885626990630958} | |
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| 0.8723 | 5.0 | 3980 | 0.9824 | {'accuracy': 0.5728643216080402} | {'f1': 0.5365052338942904} | |
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| 0.7803 | 6.0 | 4776 | 1.0071 | {'accuracy': 0.571608040201005} | {'f1': 0.5246756181464156} | |
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| 0.7198 | 7.0 | 5572 | 1.0233 | {'accuracy': 0.5741206030150754} | {'f1': 0.5405969058526473} | |
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| 0.6589 | 8.0 | 6368 | 1.0902 | {'accuracy': 0.5816582914572864} | {'f1': 0.5421523761661359} | |
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| 0.6055 | 9.0 | 7164 | 1.0980 | {'accuracy': 0.5835427135678392} | {'f1': 0.5601877104043351} | |
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| 0.5722 | 10.0 | 7960 | 1.1521 | {'accuracy': 0.5785175879396985} | {'f1': 0.565251065728165} | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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