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

Image Classification
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swinv2
Generated from Trainer
Eval Results
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  1. README.md +117 -0
  2. model.safetensors +1 -1
  3. runs/Dec03_15-39-19_279d23e2c555/events.out.tfevents.1733240382.279d23e2c555.1261.0 +2 -2
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-da-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: 0.6304347826086957
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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-da-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: 1.4456
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+ - Accuracy: 0.6304
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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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+ | 1.3823 | 0.9565 | 11 | 1.4058 | 0.1957 |
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+ | 1.3366 | 2.0 | 23 | 1.4482 | 0.1957 |
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+ | 1.2352 | 2.9565 | 34 | 1.2309 | 0.4565 |
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+ | 1.1374 | 4.0 | 46 | 1.1031 | 0.6087 |
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+ | 1.0344 | 4.9565 | 57 | 1.0230 | 0.5870 |
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+ | 0.8772 | 6.0 | 69 | 0.9115 | 0.6522 |
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+ | 0.7321 | 6.9565 | 80 | 0.8858 | 0.6522 |
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+ | 0.6319 | 8.0 | 92 | 0.8665 | 0.6522 |
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+ | 0.6438 | 8.9565 | 103 | 0.7738 | 0.7174 |
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+ | 0.4714 | 10.0 | 115 | 0.8492 | 0.6304 |
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+ | 0.433 | 10.9565 | 126 | 0.8386 | 0.6957 |
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+ | 0.4793 | 12.0 | 138 | 0.9394 | 0.7391 |
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+ | 0.4769 | 12.9565 | 149 | 0.9471 | 0.6522 |
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+ | 0.3872 | 14.0 | 161 | 1.1526 | 0.6087 |
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+ | 0.3906 | 14.9565 | 172 | 1.0575 | 0.6522 |
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+ | 0.3798 | 16.0 | 184 | 1.0593 | 0.6957 |
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+ | 0.3377 | 16.9565 | 195 | 1.0783 | 0.6087 |
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+ | 0.3919 | 18.0 | 207 | 1.1067 | 0.6522 |
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+ | 0.3631 | 18.9565 | 218 | 1.1018 | 0.6739 |
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+ | 0.2762 | 20.0 | 230 | 1.1479 | 0.6522 |
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+ | 0.2935 | 20.9565 | 241 | 1.1055 | 0.6957 |
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+ | 0.3029 | 22.0 | 253 | 1.1203 | 0.6739 |
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+ | 0.2857 | 22.9565 | 264 | 1.2820 | 0.6304 |
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+ | 0.2603 | 24.0 | 276 | 1.2550 | 0.6304 |
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+ | 0.2162 | 24.9565 | 287 | 1.1655 | 0.6739 |
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+ | 0.2465 | 26.0 | 299 | 1.2511 | 0.6739 |
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+ | 0.2238 | 26.9565 | 310 | 1.3461 | 0.6304 |
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+ | 0.2271 | 28.0 | 322 | 1.3472 | 0.6304 |
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+ | 0.2694 | 28.9565 | 333 | 1.4501 | 0.6304 |
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+ | 0.1903 | 30.0 | 345 | 1.4629 | 0.6304 |
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+ | 0.2054 | 30.9565 | 356 | 1.4672 | 0.6304 |
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+ | 0.199 | 32.0 | 368 | 1.4725 | 0.6304 |
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+ | 0.2034 | 32.9565 | 379 | 1.4507 | 0.6522 |
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+ | 0.2048 | 34.0 | 391 | 1.4330 | 0.6304 |
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+ | 0.1767 | 34.9565 | 402 | 1.4638 | 0.6304 |
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+ | 0.1799 | 36.0 | 414 | 1.4232 | 0.6304 |
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+ | 0.1903 | 36.9565 | 425 | 1.4508 | 0.6304 |
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+ | 0.1864 | 38.0 | 437 | 1.4460 | 0.6304 |
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+ | 0.1818 | 38.2609 | 440 | 1.4456 | 0.6304 |
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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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