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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV52

Image Classification
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swinv2
Generated from Trainer
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  2. model.safetensors +1 -1
  3. runs/Feb09_22-53-44_e3629502e642/events.out.tfevents.1739141636.e3629502e642.7979.0 +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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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV52
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+ results: []
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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-humeda-DAV52
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7838
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+ - Accuracy: 0.75
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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: Use OptimizerNames.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: cosine_with_restarts
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 40
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+ - mixed_precision_training: Native AMP
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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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+ | No log | 1.0 | 6 | 1.6162 | 0.2045 |
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+ | 1.3978 | 2.0 | 12 | 1.6140 | 0.1818 |
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+ | 1.3978 | 3.0 | 18 | 1.4029 | 0.4318 |
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+ | 1.0539 | 4.0 | 24 | 1.2503 | 0.5455 |
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+ | 1.0539 | 5.0 | 30 | 1.0014 | 0.625 |
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+ | 0.7171 | 6.0 | 36 | 0.9539 | 0.6364 |
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+ | 0.7171 | 7.0 | 42 | 0.9958 | 0.6136 |
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+ | 0.5557 | 8.0 | 48 | 0.8233 | 0.7386 |
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+ | 0.5557 | 9.0 | 54 | 0.8813 | 0.6136 |
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+ | 0.4942 | 10.0 | 60 | 0.8385 | 0.7159 |
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+ | 0.4942 | 11.0 | 66 | 0.7914 | 0.7614 |
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+ | 0.3957 | 12.0 | 72 | 0.7742 | 0.7273 |
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+ | 0.3957 | 13.0 | 78 | 0.8122 | 0.7045 |
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+ | 0.3664 | 14.0 | 84 | 0.7981 | 0.75 |
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+ | 0.3664 | 15.0 | 90 | 0.7852 | 0.7159 |
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+ | 0.3042 | 16.0 | 96 | 0.8829 | 0.7159 |
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+ | 0.3042 | 17.0 | 102 | 0.7630 | 0.7386 |
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+ | 0.2673 | 18.0 | 108 | 0.7936 | 0.75 |
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+ | 0.2673 | 19.0 | 114 | 0.7491 | 0.7727 |
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+ | 0.2308 | 20.0 | 120 | 0.7948 | 0.7841 |
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+ | 0.2308 | 21.0 | 126 | 0.7798 | 0.7841 |
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+ | 0.2113 | 22.0 | 132 | 0.7635 | 0.7614 |
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+ | 0.2113 | 23.0 | 138 | 0.8521 | 0.7159 |
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+ | 0.1852 | 24.0 | 144 | 0.8660 | 0.7386 |
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+ | 0.1852 | 25.0 | 150 | 0.7984 | 0.7386 |
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+ | 0.1765 | 26.0 | 156 | 0.7750 | 0.7614 |
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+ | 0.1765 | 27.0 | 162 | 0.7935 | 0.7386 |
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+ | 0.1969 | 28.0 | 168 | 0.7956 | 0.75 |
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+ | 0.1969 | 29.0 | 174 | 0.7902 | 0.7727 |
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+ | 0.1502 | 30.0 | 180 | 0.7868 | 0.7614 |
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+ | 0.1502 | 31.0 | 186 | 0.7842 | 0.7614 |
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+ | 0.1621 | 32.0 | 192 | 0.7836 | 0.75 |
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+ | 0.1621 | 33.0 | 198 | 0.7837 | 0.75 |
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+ | 0.1621 | 33.3810 | 200 | 0.7838 | 0.75 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.2
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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