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swinv2-tiny-patch4-window8-256-DMAE-da2-colab

Image Classification
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swinv2
Generated from Trainer
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README.md ADDED
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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-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-DMAE-da2-colab
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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: 0.717391304347826
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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-DMAE-da2-colab
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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: 1.2108
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+ - Accuracy: 0.7174
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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: Use 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: 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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+ | 1.4149 | 0.9565 | 11 | 1.3905 | 0.2174 |
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+ | 1.3431 | 1.9348 | 22 | 1.3828 | 0.3043 |
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+ | 1.2396 | 2.9130 | 33 | 1.2675 | 0.4348 |
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+ | 1.1377 | 3.9783 | 45 | 1.2067 | 0.3478 |
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+ | 1.0144 | 4.9565 | 56 | 0.9060 | 0.6087 |
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+ | 0.9016 | 5.9348 | 67 | 0.8025 | 0.6739 |
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+ | 0.7941 | 6.9130 | 78 | 0.7812 | 0.6957 |
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+ | 0.6986 | 7.9783 | 90 | 0.9441 | 0.5870 |
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+ | 0.6245 | 8.9565 | 101 | 0.8641 | 0.6957 |
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+ | 0.6044 | 9.9348 | 112 | 0.8648 | 0.6087 |
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+ | 0.536 | 10.9130 | 123 | 0.8800 | 0.5870 |
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+ | 0.4825 | 11.9783 | 135 | 0.8388 | 0.7391 |
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+ | 0.4972 | 12.9565 | 146 | 0.8763 | 0.7174 |
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+ | 0.4284 | 13.9348 | 157 | 0.8228 | 0.6957 |
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+ | 0.3961 | 14.9130 | 168 | 0.8260 | 0.7174 |
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+ | 0.3877 | 15.9783 | 180 | 0.9368 | 0.7609 |
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+ | 0.3744 | 16.9565 | 191 | 1.1221 | 0.6304 |
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+ | 0.3266 | 17.9348 | 202 | 1.0177 | 0.6739 |
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+ | 0.3257 | 18.9130 | 213 | 1.0300 | 0.6957 |
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+ | 0.3164 | 19.9783 | 225 | 1.1344 | 0.6957 |
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+ | 0.2965 | 20.9565 | 236 | 0.9283 | 0.7391 |
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+ | 0.293 | 21.9348 | 247 | 1.0128 | 0.6957 |
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+ | 0.2929 | 22.9130 | 258 | 1.0450 | 0.7609 |
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+ | 0.2878 | 23.9783 | 270 | 1.1482 | 0.7174 |
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+ | 0.2447 | 24.9565 | 281 | 1.0716 | 0.7174 |
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+ | 0.2601 | 25.9348 | 292 | 1.0770 | 0.6957 |
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+ | 0.2299 | 26.9130 | 303 | 1.1769 | 0.7391 |
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+ | 0.2401 | 27.9783 | 315 | 1.1407 | 0.7174 |
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+ | 0.2347 | 28.9565 | 326 | 1.1929 | 0.6957 |
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+ | 0.2584 | 29.9348 | 337 | 1.0957 | 0.6739 |
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+ | 0.2204 | 30.9130 | 348 | 1.1721 | 0.6739 |
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+ | 0.2031 | 31.9783 | 360 | 1.0843 | 0.6739 |
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+ | 0.2241 | 32.9565 | 371 | 1.1350 | 0.6957 |
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+ | 0.1798 | 33.9348 | 382 | 1.2419 | 0.6957 |
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+ | 0.2435 | 34.9130 | 393 | 1.1522 | 0.6957 |
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+ | 0.1857 | 35.9783 | 405 | 1.1207 | 0.6957 |
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+ | 0.1889 | 36.9565 | 416 | 1.1711 | 0.6957 |
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+ | 0.2043 | 37.9348 | 427 | 1.1978 | 0.6957 |
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+ | 0.1951 | 38.9130 | 438 | 1.2107 | 0.7174 |
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+ | 0.1901 | 39.1087 | 440 | 1.2108 | 0.7174 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.2
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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