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--- |
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base_model: openai/clip-vit-base-patch32 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: ktp-kk-crop |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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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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# ktp-kk-crop |
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0312 |
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- Accuracy: 1.0 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 25 |
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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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| No log | 0.8696 | 5 | 0.5871 | 0.7 | |
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| No log | 1.9130 | 11 | 0.0729 | 0.9667 | |
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| 0.7676 | 2.9565 | 17 | 0.1986 | 0.9 | |
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| 0.7676 | 4.0 | 23 | 0.1610 | 0.9 | |
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| 0.7676 | 4.8696 | 28 | 0.0644 | 0.9667 | |
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| 0.2441 | 5.9130 | 34 | 0.2016 | 0.9 | |
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| 0.2441 | 6.9565 | 40 | 0.1530 | 0.9 | |
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| 0.1751 | 8.0 | 46 | 0.0412 | 1.0 | |
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| 0.1751 | 8.8696 | 51 | 0.0301 | 1.0 | |
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| 0.1751 | 9.9130 | 57 | 0.0495 | 0.9667 | |
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| 0.1156 | 10.9565 | 63 | 0.0283 | 1.0 | |
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| 0.1156 | 12.0 | 69 | 0.0214 | 1.0 | |
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| 0.1156 | 12.8696 | 74 | 0.1014 | 0.9667 | |
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| 0.1238 | 13.9130 | 80 | 0.0538 | 1.0 | |
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| 0.1238 | 14.9565 | 86 | 0.0477 | 1.0 | |
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| 0.1064 | 16.0 | 92 | 0.0105 | 1.0 | |
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| 0.1064 | 16.8696 | 97 | 0.0389 | 0.9667 | |
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| 0.1064 | 17.9130 | 103 | 0.0120 | 1.0 | |
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| 0.0862 | 18.9565 | 109 | 0.0183 | 1.0 | |
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| 0.0862 | 20.0 | 115 | 0.0259 | 1.0 | |
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| 0.0345 | 20.8696 | 120 | 0.0272 | 1.0 | |
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| 0.0345 | 21.7391 | 125 | 0.0312 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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