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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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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: vit-base-patch16-224-finetuned-eurosat
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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: 0.972
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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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+ # vit-base-patch16-224-finetuned-eurosat
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1017
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+ - Accuracy: 0.972
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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: 450
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+ - eval_batch_size: 450
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 1800
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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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+
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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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+ | 0.3243 | 1.0 | 46 | 0.2033 | 0.944 |
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+ | 0.1247 | 2.0 | 92 | 0.0791 | 0.976 |
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+ | 0.0937 | 3.0 | 138 | 0.0971 | 0.963 |
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+ | 0.0716 | 4.0 | 184 | 0.0778 | 0.972 |
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+ | 0.0543 | 5.0 | 230 | 0.0654 | 0.98 |
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+ | 0.0367 | 6.0 | 276 | 0.0913 | 0.972 |
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+ | 0.0292 | 7.0 | 322 | 0.0778 | 0.979 |
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+ | 0.0204 | 8.0 | 368 | 0.0914 | 0.971 |
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+ | 0.0161 | 9.0 | 414 | 0.1026 | 0.971 |
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+ | 0.0154 | 10.0 | 460 | 0.1017 | 0.972 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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