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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-crop-clip |
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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: 0.9864864864864865 |
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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-crop-clip |
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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: 0.1223 |
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- Accuracy: 0.9865 |
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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: 20 |
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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 | 0.96 | 6 | 0.8954 | 0.5270 | |
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| 0.7112 | 1.92 | 12 | 0.6729 | 0.5405 | |
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| 0.7112 | 2.88 | 18 | 0.6407 | 0.7297 | |
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| 0.4413 | 4.0 | 25 | 0.1279 | 0.9459 | |
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| 0.0935 | 4.96 | 31 | 0.1436 | 0.9730 | |
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| 0.0935 | 5.92 | 37 | 0.0021 | 1.0 | |
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| 0.0697 | 6.88 | 43 | 0.2862 | 0.9459 | |
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| 0.161 | 8.0 | 50 | 0.0843 | 0.9595 | |
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| 0.161 | 8.96 | 56 | 0.2255 | 0.9459 | |
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| 0.0061 | 9.92 | 62 | 0.4678 | 0.9054 | |
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| 0.0061 | 10.88 | 68 | 0.3299 | 0.9189 | |
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| 0.0309 | 12.0 | 75 | 0.5189 | 0.9189 | |
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| 0.0025 | 12.96 | 81 | 0.0850 | 0.9865 | |
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| 0.0025 | 13.92 | 87 | 0.0720 | 0.9865 | |
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| 0.0042 | 14.88 | 93 | 0.0745 | 0.9865 | |
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| 0.0002 | 16.0 | 100 | 0.0869 | 0.9865 | |
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| 0.0002 | 16.96 | 106 | 0.0895 | 0.9865 | |
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| 0.0001 | 17.92 | 112 | 0.1127 | 0.9865 | |
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| 0.0001 | 18.88 | 118 | 0.1219 | 0.9865 | |
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| 0.0 | 19.2 | 120 | 0.1223 | 0.9865 | |
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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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