| | --- |
| | base_model: openai/clip-vit-base-patch32 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: document-spoof-clip |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: imagefolder |
| | type: imagefolder |
| | config: default |
| | split: validation |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9857142857142858 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # document-spoof-clip |
| |
|
| | 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. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0225 |
| | - Accuracy: 0.9857 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-------:|:----:|:---------------:|:--------:| |
| | | No log | 0.8421 | 4 | 0.5305 | 0.8429 | |
| | | No log | 1.8947 | 9 | 0.1707 | 0.9286 | |
| | | 0.6268 | 2.9474 | 14 | 0.3507 | 0.8429 | |
| | | 0.6268 | 4.0 | 19 | 0.4707 | 0.8 | |
| | | 0.2881 | 4.8421 | 23 | 0.1337 | 0.9286 | |
| | | 0.2881 | 5.8947 | 28 | 0.1293 | 0.9286 | |
| | | 0.2349 | 6.9474 | 33 | 0.0565 | 0.9714 | |
| | | 0.2349 | 8.0 | 38 | 0.0676 | 0.9571 | |
| | | 0.0907 | 8.8421 | 42 | 0.3071 | 0.9 | |
| | | 0.0907 | 9.8947 | 47 | 0.1462 | 0.9714 | |
| | | 0.1203 | 10.9474 | 52 | 0.0761 | 0.9714 | |
| | | 0.1203 | 12.0 | 57 | 0.0808 | 0.9571 | |
| | | 0.0715 | 12.8421 | 61 | 0.0204 | 0.9857 | |
| | | 0.0715 | 13.8947 | 66 | 0.0210 | 0.9857 | |
| | | 0.031 | 14.9474 | 71 | 0.0274 | 0.9714 | |
| | | 0.031 | 16.0 | 76 | 0.0448 | 0.9857 | |
| | | 0.0655 | 16.8421 | 80 | 0.0225 | 0.9857 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.41.2 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.19.2 |
| | - Tokenizers 0.19.1 |
| |
|
