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swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max

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Files changed (11) hide show
  1. README.md +96 -0
  2. all_results.json +12 -0
  3. config.json +56 -0
  4. eval_results.json +8 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +22 -0
  7. runs/Dec28_19-33-23_MacBook-Pro-de-Max-2.local/events.out.tfevents.1703788410.MacBook-Pro-de-Max-2.local.35333.0 +3 -0
  8. runs/Dec28_19-33-23_MacBook-Pro-de-Max-2.local/events.out.tfevents.1703789774.MacBook-Pro-de-Max-2.local.35333.1 +3 -0
  9. train_results.json +7 -0
  10. trainer_state.json +333 -0
  11. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/swinv2-tiny-patch4-window8-256
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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: swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max
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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: 0.594017094017094
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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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+ # swinv2-tiny-patch4-window8-256-finetuned-gardner-te-max
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+
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+ This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8795
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+ - Accuracy: 0.5940
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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: 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: 20
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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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+ | 1.0943 | 0.94 | 11 | 1.0750 | 0.6325 |
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+ | 0.9996 | 1.96 | 23 | 0.8011 | 0.6325 |
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+ | 0.7731 | 2.98 | 35 | 0.7182 | 0.6325 |
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+ | 0.7564 | 4.0 | 47 | 0.7109 | 0.6325 |
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+ | 0.7331 | 4.94 | 58 | 0.7026 | 0.6325 |
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+ | 0.7336 | 5.96 | 70 | 0.6848 | 0.6325 |
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+ | 0.7305 | 6.98 | 82 | 0.6938 | 0.6325 |
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+ | 0.7314 | 8.0 | 94 | 0.6549 | 0.6325 |
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+ | 0.6905 | 8.94 | 105 | 0.6364 | 0.6867 |
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+ | 0.7315 | 9.96 | 117 | 0.6223 | 0.6687 |
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+ | 0.6839 | 10.98 | 129 | 0.6528 | 0.7530 |
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+ | 0.6931 | 12.0 | 141 | 0.6209 | 0.7410 |
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+ | 0.6705 | 12.94 | 152 | 0.6296 | 0.7169 |
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+ | 0.7227 | 13.96 | 164 | 0.6039 | 0.7108 |
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+ | 0.6695 | 14.98 | 176 | 0.6049 | 0.7530 |
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+ | 0.6981 | 16.0 | 188 | 0.5965 | 0.7048 |
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+ | 0.6566 | 16.94 | 199 | 0.6111 | 0.7410 |
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+ | 0.6828 | 17.96 | 211 | 0.5969 | 0.7530 |
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+ | 0.6632 | 18.72 | 220 | 0.5947 | 0.7530 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.16.0
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+ - Tokenizers 0.15.0
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