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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-base-patch4-window7-224-in22k |
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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: swin-base-patch4-window7-224-in22k-newly-trained |
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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: train |
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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: 0.959 |
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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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# swin-base-patch4-window7-224-in22k-newly-trained |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1335 |
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- Accuracy: 0.959 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 1 |
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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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| 2.2459 | 0.14 | 10 | 1.7346 | 0.575 | |
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| 1.4338 | 0.28 | 20 | 0.7222 | 0.841 | |
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| 0.8059 | 0.43 | 30 | 0.3252 | 0.915 | |
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| 0.5772 | 0.57 | 40 | 0.2071 | 0.942 | |
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| 0.5599 | 0.71 | 50 | 0.1553 | 0.958 | |
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| 0.4473 | 0.85 | 60 | 0.1373 | 0.958 | |
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| 0.4292 | 0.99 | 70 | 0.1335 | 0.959 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.1 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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