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SW2-DMAE-DA

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swinv2
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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: 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: SW2-DMAE-DA
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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: 0.6086956521739131
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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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+ # SW2-DMAE-DA
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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: 0.8387
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+ - Accuracy: 0.6087
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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: 4e-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: 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: 40
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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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+ | 6.464 | 0.96 | 11 | 7.9190 | 0.1087 |
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+ | 6.5496 | 2.0 | 23 | 7.5824 | 0.1087 |
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+ | 6.1541 | 2.96 | 34 | 6.4156 | 0.1087 |
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+ | 5.2649 | 4.0 | 46 | 4.5067 | 0.1087 |
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+ | 4.1175 | 4.96 | 57 | 2.7241 | 0.1087 |
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+ | 2.8424 | 6.0 | 69 | 1.5623 | 0.1087 |
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+ | 1.4376 | 6.96 | 80 | 1.4003 | 0.1087 |
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+ | 1.4054 | 8.0 | 92 | 1.4055 | 0.1087 |
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+ | 1.3798 | 8.96 | 103 | 1.3466 | 0.4565 |
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+ | 1.3331 | 10.0 | 115 | 1.4385 | 0.1522 |
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+ | 1.2736 | 10.96 | 126 | 1.3106 | 0.2391 |
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+ | 1.2127 | 12.0 | 138 | 1.2908 | 0.1957 |
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+ | 1.2531 | 12.96 | 149 | 1.2545 | 0.5 |
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+ | 1.0972 | 14.0 | 161 | 1.2515 | 0.3478 |
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+ | 1.0029 | 14.96 | 172 | 1.2238 | 0.2609 |
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+ | 1.0141 | 16.0 | 184 | 1.2067 | 0.3696 |
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+ | 0.9129 | 16.96 | 195 | 1.1149 | 0.5652 |
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+ | 0.9157 | 18.0 | 207 | 1.1957 | 0.3913 |
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+ | 0.8516 | 18.96 | 218 | 1.0034 | 0.5435 |
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+ | 0.7804 | 20.0 | 230 | 0.9991 | 0.4783 |
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+ | 0.7328 | 20.96 | 241 | 0.9840 | 0.5870 |
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+ | 0.7101 | 22.0 | 253 | 0.9661 | 0.5435 |
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+ | 0.7099 | 22.96 | 264 | 0.9392 | 0.5435 |
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+ | 0.7238 | 24.0 | 276 | 0.9553 | 0.5 |
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+ | 0.6605 | 24.96 | 287 | 0.9571 | 0.5435 |
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+ | 0.639 | 26.0 | 299 | 1.0534 | 0.5652 |
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+ | 0.6123 | 26.96 | 310 | 0.9152 | 0.6087 |
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+ | 0.6021 | 28.0 | 322 | 0.8704 | 0.5870 |
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+ | 0.5971 | 28.96 | 333 | 0.8726 | 0.5652 |
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+ | 0.5413 | 30.0 | 345 | 0.8287 | 0.5870 |
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+ | 0.5663 | 30.96 | 356 | 0.9271 | 0.5435 |
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+ | 0.5343 | 32.0 | 368 | 0.8856 | 0.6304 |
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+ | 0.525 | 32.96 | 379 | 0.8579 | 0.6087 |
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+ | 0.5447 | 34.0 | 391 | 0.8746 | 0.5870 |
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+ | 0.5036 | 34.96 | 402 | 0.8684 | 0.5652 |
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+ | 0.4918 | 36.0 | 414 | 0.8268 | 0.5870 |
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+ | 0.503 | 36.96 | 425 | 0.8374 | 0.5870 |
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+ | 0.5114 | 38.0 | 437 | 0.8380 | 0.6087 |
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+ | 0.5272 | 38.26 | 440 | 0.8387 | 0.6087 |
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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+cu118
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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