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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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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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model-index: |
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- name: swin-tiny-patch4-window7-224-finetuned-plant-classification |
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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: 0.7557471264367817 |
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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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# swin-tiny-patch4-window7-224-finetuned-plant-classification |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6592 |
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- Accuracy: 0.7557 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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| 0.8257 | 1.0 | 268 | 0.7941 | 0.6695 | |
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| 0.7235 | 2.0 | 537 | 0.7696 | 0.6695 | |
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| 0.6939 | 3.0 | 806 | 0.7428 | 0.6724 | |
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| 0.665 | 4.0 | 1075 | 0.6884 | 0.7328 | |
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| 0.6846 | 5.0 | 1343 | 0.7144 | 0.6954 | |
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| 0.6391 | 6.0 | 1612 | 0.6854 | 0.7155 | |
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| 0.6172 | 7.0 | 1881 | 0.6698 | 0.7011 | |
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| 0.6332 | 8.0 | 2150 | 0.6510 | 0.7126 | |
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| 0.5679 | 9.0 | 2418 | 0.6323 | 0.7299 | |
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| 0.5109 | 10.0 | 2687 | 0.6629 | 0.7098 | |
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| 0.5594 | 11.0 | 2956 | 0.6556 | 0.7270 | |
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| 0.4874 | 12.0 | 3225 | 0.6627 | 0.7155 | |
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| 0.4687 | 13.0 | 3493 | 0.6645 | 0.7299 | |
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| 0.4686 | 14.0 | 3762 | 0.6469 | 0.7213 | |
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| 0.4862 | 15.0 | 4031 | 0.6602 | 0.7356 | |
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| 0.4432 | 16.0 | 4300 | 0.6550 | 0.7270 | |
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| 0.4368 | 17.0 | 4568 | 0.6472 | 0.7385 | |
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| 0.3815 | 18.0 | 4837 | 0.6557 | 0.7557 | |
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| 0.3674 | 19.0 | 5106 | 0.6638 | 0.7529 | |
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| 0.4224 | 19.94 | 5360 | 0.6592 | 0.7557 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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