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--- |
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license: apache-2.0 |
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base_model: xiaopch/vit-base-patch16-224-finetuned |
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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: vit-base-patch16-224-finetuned-for-agricultural |
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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: 0.7309236947791165 |
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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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# vit-base-patch16-224-finetuned-for-agricultural |
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This model is a fine-tuned version of [xiaopch/vit-base-patch16-224-finetuned](https://huggingface.co/xiaopch/vit-base-patch16-224-finetuned) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9246 |
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- Accuracy: 0.7309 |
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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: 10 |
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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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| 0.9131 | 1.0 | 35 | 1.0878 | 0.6847 | |
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| 0.8066 | 2.0 | 70 | 0.9933 | 0.7189 | |
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| 0.7259 | 3.0 | 105 | 0.9445 | 0.7249 | |
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| 0.6719 | 4.0 | 140 | 0.9246 | 0.7309 | |
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| 0.6056 | 5.0 | 175 | 0.9258 | 0.7229 | |
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| 0.5576 | 6.0 | 210 | 0.9230 | 0.7309 | |
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| 0.5113 | 7.0 | 245 | 0.9152 | 0.7169 | |
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| 0.488 | 8.0 | 280 | 0.9119 | 0.7209 | |
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| 0.4822 | 9.0 | 315 | 0.9061 | 0.7269 | |
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| 0.4163 | 10.0 | 350 | 0.9039 | 0.7289 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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