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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_concate_fusin_gpt
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/f4ofem3j)
# aoi_clip_high_resolution_concate_fusin_gpt
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: 4.2052
- Accuracy: 0.0731
## 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: 20
- 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: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.3923 | 9.9872 | 3110 | 3.0972 | 0.0765 |
| 2.1272 | 19.9743 | 6220 | 3.5858 | 0.0782 |
| 1.9949 | 29.9615 | 9330 | 3.6033 | 0.0785 |
| 1.957 | 39.9486 | 12440 | 3.7208 | 0.0769 |
| 1.9313 | 49.9358 | 15550 | 3.8174 | 0.0759 |
| 1.9255 | 59.9229 | 18660 | 3.9145 | 0.0748 |
| 1.9179 | 69.9101 | 21770 | 4.0367 | 0.0746 |
| 1.9133 | 79.8972 | 24880 | 4.0690 | 0.0740 |
| 1.9102 | 89.8844 | 27990 | 4.1227 | 0.0737 |
| 1.9084 | 99.8715 | 31100 | 4.2052 | 0.0734 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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