---
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_random_sampler
  results: []
---

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[<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/4jms502r)
# aoi_clip_high_resolution_concate_fusin_gpt_random_sampler

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: 2.9963
- Accuracy: 0.0500

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 0.0008        | 9.9872  | 3110  | 3.0008          | 0.0500   |
| 0.0007        | 19.9743 | 6220  | 2.9971          | 0.0501   |
| 0.0007        | 29.9615 | 9330  | 2.9971          | 0.0501   |
| 0.0007        | 39.9486 | 12440 | 2.9988          | 0.0500   |
| 0.0007        | 49.9358 | 15550 | 2.9968          | 0.0499   |
| 0.0007        | 59.9229 | 18660 | 2.9966          | 0.0502   |
| 0.0007        | 69.9101 | 21770 | 2.9961          | 0.0503   |
| 0.0007        | 79.8972 | 24880 | 2.9967          | 0.0503   |
| 0.0007        | 89.8844 | 27990 | 2.9966          | 0.0503   |
| 0.0007        | 99.8715 | 31100 | 2.9963          | 0.0502   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1