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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_concate_fusin
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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/1va6rx5c)
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# aoi_clip_high_resolution_concate_fusin
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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: 4.6300
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- Accuracy: 0.0310
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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: 40
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- eval_batch_size: 40
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- seed: 42
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- gradient_accumulation_steps: 5
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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: 60.0
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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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| 2.4434 | 5.9923 | 1866 | 3.7035 | 0.0316 |
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| 2.2689 | 11.9846 | 3732 | 3.9282 | 0.0312 |
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| 2.1311 | 17.9769 | 5598 | 4.1890 | 0.0324 |
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| 2.0473 | 23.9692 | 7464 | 4.2218 | 0.0317 |
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| 2.0065 | 29.9615 | 9330 | 4.1968 | 0.0317 |
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| 1.9816 | 35.9538 | 11196 | 4.3277 | 0.0311 |
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| 1.9593 | 41.9461 | 13062 | 4.4400 | 0.0312 |
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| 1.9448 | 47.9383 | 14928 | 4.4896 | 0.0311 |
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| 1.9352 | 53.9306 | 16794 | 4.5710 | 0.0311 |
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| 1.9342 | 59.9229 | 18660 | 4.6300 | 0.0310 |
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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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