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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: document-spoof-clip |
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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: 0.9857142857142858 |
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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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# document-spoof-clip |
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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: 0.1338 |
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- Accuracy: 0.9857 |
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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: 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 | 0.8421 | 4 | 0.6403 | 0.6571 | |
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| No log | 1.8947 | 9 | 0.9389 | 0.6714 | |
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| 0.572 | 2.9474 | 14 | 0.2936 | 0.8857 | |
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| 0.572 | 4.0 | 19 | 0.6845 | 0.8143 | |
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| 0.4928 | 4.8421 | 23 | 0.0334 | 0.9857 | |
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| 0.4928 | 5.8947 | 28 | 0.1273 | 0.9571 | |
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| 0.0987 | 6.9474 | 33 | 0.0738 | 0.9857 | |
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| 0.0987 | 8.0 | 38 | 0.1519 | 0.9571 | |
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| 0.017 | 8.8421 | 42 | 0.0569 | 0.9714 | |
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| 0.017 | 9.8947 | 47 | 0.1164 | 0.9857 | |
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| 0.0062 | 10.9474 | 52 | 0.0672 | 0.9714 | |
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| 0.0062 | 12.0 | 57 | 0.0446 | 0.9714 | |
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| 0.0084 | 12.8421 | 61 | 0.0882 | 0.9857 | |
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| 0.0084 | 13.8947 | 66 | 0.1117 | 0.9714 | |
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| 0.0 | 14.9474 | 71 | 0.1420 | 0.9714 | |
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| 0.0 | 16.0 | 76 | 0.1360 | 0.9714 | |
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| 0.0001 | 16.8421 | 80 | 0.1338 | 0.9857 | |
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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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