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--- |
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base_model: openai/clip-vit-base-patch32 |
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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: fotocopy-ori |
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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: validation |
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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: 0.9491525423728814 |
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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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# fotocopy-ori |
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4776 |
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- Accuracy: 0.9492 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 25 |
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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 | 0.9231 | 3 | 0.6343 | 0.4915 | |
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| No log | 1.8462 | 6 | 0.2235 | 0.9322 | |
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| No log | 2.7692 | 9 | 0.1887 | 0.9492 | |
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| No log | 4.0 | 13 | 0.0278 | 0.9831 | |
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| 0.4375 | 4.9231 | 16 | 1.6119 | 0.8475 | |
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| 0.4375 | 5.8462 | 19 | 0.5158 | 0.8983 | |
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| 0.4375 | 6.7692 | 22 | 0.0602 | 0.9661 | |
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| 0.4375 | 8.0 | 26 | 0.3831 | 0.9492 | |
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| 0.4375 | 8.9231 | 29 | 0.4555 | 0.9492 | |
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| 0.1245 | 9.8462 | 32 | 0.9890 | 0.9153 | |
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| 0.1245 | 10.7692 | 35 | 0.4632 | 0.9322 | |
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| 0.1245 | 12.0 | 39 | 0.5992 | 0.9322 | |
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| 0.1245 | 12.9231 | 42 | 0.6255 | 0.9322 | |
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| 0.048 | 13.8462 | 45 | 0.5156 | 0.9492 | |
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| 0.048 | 14.7692 | 48 | 0.6033 | 0.9492 | |
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| 0.048 | 16.0 | 52 | 0.5978 | 0.9492 | |
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| 0.048 | 16.9231 | 55 | 0.5747 | 0.9492 | |
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| 0.048 | 17.8462 | 58 | 0.5635 | 0.9492 | |
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| 0.0005 | 18.7692 | 61 | 0.5314 | 0.9492 | |
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| 0.0005 | 20.0 | 65 | 0.5023 | 0.9492 | |
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| 0.0005 | 20.9231 | 68 | 0.4886 | 0.9492 | |
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| 0.0005 | 21.8462 | 71 | 0.4809 | 0.9492 | |
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| 0.0005 | 22.7692 | 74 | 0.4779 | 0.9492 | |
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| 0.0 | 23.0769 | 75 | 0.4776 | 0.9492 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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