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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: ktp-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: 0.9852941176470589 |
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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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# ktp-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: 0.0740 |
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- Accuracy: 0.9853 |
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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: 15 |
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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 | 0.8889 | 4 | 0.5501 | 0.8088 | |
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| No log | 2.0 | 9 | 0.3671 | 0.8529 | |
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| 0.5611 | 2.8889 | 13 | 0.3852 | 0.8235 | |
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| 0.5611 | 4.0 | 18 | 0.2422 | 0.9118 | |
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| 0.4558 | 4.8889 | 22 | 0.3534 | 0.8824 | |
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| 0.4558 | 6.0 | 27 | 0.1137 | 0.9412 | |
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| 0.3562 | 6.8889 | 31 | 0.5266 | 0.7941 | |
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| 0.3562 | 8.0 | 36 | 0.1918 | 0.9118 | |
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| 0.1201 | 8.8889 | 40 | 0.0301 | 1.0 | |
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| 0.1201 | 10.0 | 45 | 0.0450 | 0.9853 | |
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| 0.1201 | 10.8889 | 49 | 0.0327 | 0.9853 | |
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| 0.0604 | 12.0 | 54 | 0.0898 | 0.9706 | |
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| 0.0604 | 12.8889 | 58 | 0.0789 | 0.9853 | |
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| 0.0322 | 13.3333 | 60 | 0.0740 | 0.9853 | |
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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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