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update model card README.md
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README.md
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---
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license: apache-2.0
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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: segformer-class-classWeights-augmentation
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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: 0.9545454545454546
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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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# segformer-class-classWeights-augmentation
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0927
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- Accuracy: 0.9545
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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: 10
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- eval_batch_size: 10
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 40
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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: 10
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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 | 0.9944 | 0.4773 |
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| 1.0159 | 1.92 | 12 | 0.6948 | 0.75 |
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| 1.0159 | 2.88 | 18 | 0.3417 | 0.9318 |
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| 0.5444 | 4.0 | 25 | 0.2642 | 0.9091 |
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| 0.2331 | 4.96 | 31 | 0.0986 | 0.9545 |
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| 0.2331 | 5.92 | 37 | 0.1735 | 0.9545 |
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| 0.2596 | 6.88 | 43 | 0.1091 | 0.9545 |
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| 0.1737 | 8.0 | 50 | 0.0685 | 0.9545 |
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| 0.1737 | 8.96 | 56 | 0.0995 | 0.9545 |
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| 0.1716 | 9.6 | 60 | 0.0927 | 0.9545 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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