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End of training

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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: microsoft/swinv2-tiny-patch4-window16-256
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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: my_awesome_food_model
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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: test
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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: 0.7123966942148761
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+ ---
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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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+
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+ # my_awesome_food_model
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+
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+ This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6589
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+ - Accuracy: 0.7124
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 3.2193 | 1.0 | 76 | 0.7354 | 0.6446 |
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+ | 2.7694 | 2.0 | 152 | 0.6906 | 0.6909 |
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+ | 2.9082 | 2.9637 | 225 | 0.6589 | 0.7124 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.1
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0