---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aoi_clip_high_resolution_crossAttenttionFusion_fusin_new_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/8q4tic24)
# aoi_clip_high_resolution_crossAttenttionFusion_fusin_new_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: 4.5135
- Accuracy: 0.0583

## 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: 25
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 8
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.156         | 9.9880  | 3110  | 2.9957          | 0.0492   |
| 2.1411        | 19.9759 | 6220  | 3.0536          | 0.0537   |
| 1.9888        | 29.9639 | 9330  | 3.3324          | 0.0567   |
| 1.8759        | 39.9518 | 12440 | 3.6092          | 0.0571   |
| 1.8129        | 49.9398 | 15550 | 3.8091          | 0.0575   |
| 1.7708        | 59.9277 | 18660 | 3.9898          | 0.0578   |
| 1.7413        | 69.9157 | 21770 | 4.2735          | 0.0579   |
| 1.7172        | 79.9037 | 24880 | 4.3434          | 0.0580   |
| 1.7056        | 89.8916 | 27990 | 4.5120          | 0.0581   |
| 1.7018        | 99.8796 | 31100 | 4.5135          | 0.0582   |


### Framework versions

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