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---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: fotocopy-ori
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: 0.9491525423728814
---
<!-- 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. -->
# fotocopy-ori
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: 0.4776
- Accuracy: 0.9492
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 3 | 0.6343 | 0.4915 |
| No log | 1.8462 | 6 | 0.2235 | 0.9322 |
| No log | 2.7692 | 9 | 0.1887 | 0.9492 |
| No log | 4.0 | 13 | 0.0278 | 0.9831 |
| 0.4375 | 4.9231 | 16 | 1.6119 | 0.8475 |
| 0.4375 | 5.8462 | 19 | 0.5158 | 0.8983 |
| 0.4375 | 6.7692 | 22 | 0.0602 | 0.9661 |
| 0.4375 | 8.0 | 26 | 0.3831 | 0.9492 |
| 0.4375 | 8.9231 | 29 | 0.4555 | 0.9492 |
| 0.1245 | 9.8462 | 32 | 0.9890 | 0.9153 |
| 0.1245 | 10.7692 | 35 | 0.4632 | 0.9322 |
| 0.1245 | 12.0 | 39 | 0.5992 | 0.9322 |
| 0.1245 | 12.9231 | 42 | 0.6255 | 0.9322 |
| 0.048 | 13.8462 | 45 | 0.5156 | 0.9492 |
| 0.048 | 14.7692 | 48 | 0.6033 | 0.9492 |
| 0.048 | 16.0 | 52 | 0.5978 | 0.9492 |
| 0.048 | 16.9231 | 55 | 0.5747 | 0.9492 |
| 0.048 | 17.8462 | 58 | 0.5635 | 0.9492 |
| 0.0005 | 18.7692 | 61 | 0.5314 | 0.9492 |
| 0.0005 | 20.0 | 65 | 0.5023 | 0.9492 |
| 0.0005 | 20.9231 | 68 | 0.4886 | 0.9492 |
| 0.0005 | 21.8462 | 71 | 0.4809 | 0.9492 |
| 0.0005 | 22.7692 | 74 | 0.4779 | 0.9492 |
| 0.0 | 23.0769 | 75 | 0.4776 | 0.9492 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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