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---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aoi_clip_high_resolution
results: []
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shark_meow_team/huggingface/runs/p4n09c36)
# aoi_clip_high_resolution
This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7487
- Accuracy: 0.0334
## 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: 1e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.4298 | 5.9923 | 1866 | 3.7282 | 0.0434 |
| 2.246 | 11.9846 | 3732 | 3.9666 | 0.0379 |
| 2.1293 | 17.9769 | 5598 | 4.1019 | 0.0360 |
| 2.0539 | 23.9692 | 7464 | 4.2821 | 0.0352 |
| 2.0135 | 29.9615 | 9330 | 4.3318 | 0.0345 |
| 1.9879 | 35.9538 | 11196 | 4.3700 | 0.0341 |
| 1.9648 | 41.9461 | 13062 | 4.4619 | 0.0341 |
| 1.9495 | 47.9383 | 14928 | 4.5999 | 0.0341 |
| 1.9391 | 53.9306 | 16794 | 4.6806 | 0.0339 |
| 1.9372 | 59.9229 | 18660 | 4.7487 | 0.0336 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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