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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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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: MoE-leaf-disease-convnextv2-base-1k-224 |
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results: [] |
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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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# MoE-leaf-disease-convnextv2-base-1k-224 |
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This model is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3758 |
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- Accuracy: 0.8762 |
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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: 2000 |
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- eval_batch_size: 2000 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8000 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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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| No log | 0.8 | 2 | 0.4078 | 0.8724 | |
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| No log | 2.0 | 5 | 0.3771 | 0.8776 | |
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| No log | 2.8 | 7 | 0.3776 | 0.8762 | |
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| 0.3197 | 4.0 | 10 | 0.3773 | 0.8762 | |
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| 0.3197 | 4.8 | 12 | 0.3768 | 0.8762 | |
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| 0.3197 | 6.0 | 15 | 0.3762 | 0.8762 | |
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| 0.3197 | 6.8 | 17 | 0.3760 | 0.8762 | |
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| 0.2995 | 8.0 | 20 | 0.3759 | 0.8762 | |
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| 0.2995 | 8.8 | 22 | 0.3759 | 0.8762 | |
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| 0.2995 | 10.0 | 25 | 0.3758 | 0.8762 | |
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| 0.2995 | 10.8 | 27 | 0.3758 | 0.8762 | |
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| 0.2996 | 12.0 | 30 | 0.3758 | 0.8762 | |
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| 0.2996 | 12.8 | 32 | 0.3758 | 0.8762 | |
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| 0.2996 | 14.0 | 35 | 0.3758 | 0.8762 | |
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| 0.2996 | 14.8 | 37 | 0.3758 | 0.8762 | |
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| 0.3024 | 16.0 | 40 | 0.3758 | 0.8762 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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