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README.md
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---
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base_model: OFA-Sys/chinese-clip-vit-base-patch16
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: aoi_clip_high_resolution_crossAttenttionFusion_gpt_froce_same_aoi_256_256
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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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/p8vqsimg)
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# aoi_clip_high_resolution_crossAttenttionFusion_gpt_froce_same_aoi_256_256
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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.
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It achieves the following results on the evaluation set:
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- Loss: 8.0173
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- Accuracy: 0.0636
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 25
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- eval_batch_size: 20
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 200
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 200.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:--------:|:----:|:---------------:|:--------:|
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| 1.145 | 19.9458 | 920 | 3.4201 | 0.0648 |
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| 0.8113 | 39.8916 | 1840 | 4.7945 | 0.0656 |
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| 0.6672 | 59.8374 | 2760 | 5.8494 | 0.0621 |
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| 0.5974 | 79.7832 | 3680 | 6.6827 | 0.0609 |
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| 0.5557 | 99.7290 | 4600 | 7.2286 | 0.0623 |
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| 0.5305 | 119.6748 | 5520 | 8.1406 | 0.0628 |
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| 0.5093 | 139.6206 | 6440 | 7.8770 | 0.0635 |
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| 0.4975 | 159.5664 | 7360 | 7.9540 | 0.0631 |
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| 0.4903 | 179.5122 | 8280 | 7.8321 | 0.0632 |
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| 0.481 | 199.4580 | 9200 | 8.0173 | 0.0636 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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