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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV34

Image Classification
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swinv2
Generated from Trainer
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  2. model.safetensors +1 -1
  3. runs/Jan27_05-23-33_937ed96f5f50/events.out.tfevents.1737955436.937ed96f5f50.2951.0 +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-DAV34
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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-DAV34
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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: 1.3276
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+ - Accuracy: 0.6333
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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: 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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+ | No log | 0.8889 | 6 | 1.4254 | 0.45 |
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+ | 5.9772 | 1.8889 | 12 | 1.3083 | 0.45 |
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+ | 5.9772 | 2.8889 | 18 | 1.3032 | 0.4667 |
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+ | 5.1141 | 3.8889 | 24 | 1.1521 | 0.5667 |
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+ | 5.1141 | 4.8889 | 30 | 1.2171 | 0.4667 |
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+ | 3.8959 | 5.8889 | 36 | 1.1281 | 0.4833 |
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+ | 3.8959 | 6.8889 | 42 | 0.9969 | 0.6167 |
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+ | 2.9663 | 7.8889 | 48 | 0.9814 | 0.65 |
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+ | 2.9663 | 8.8889 | 54 | 0.8797 | 0.6333 |
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+ | 2.416 | 9.8889 | 60 | 0.8956 | 0.6667 |
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+ | 2.416 | 10.8889 | 66 | 0.8364 | 0.7 |
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+ | 1.9336 | 11.8889 | 72 | 0.9800 | 0.65 |
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+ | 1.9336 | 12.8889 | 78 | 0.8707 | 0.6833 |
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+ | 1.5631 | 13.8889 | 84 | 0.9331 | 0.6333 |
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+ | 1.5631 | 14.8889 | 90 | 1.0113 | 0.6333 |
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+ | 1.1295 | 15.8889 | 96 | 1.0988 | 0.6167 |
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+ | 1.1295 | 16.8889 | 102 | 1.0197 | 0.6833 |
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+ | 1.1454 | 17.8889 | 108 | 1.2170 | 0.6167 |
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+ | 1.1454 | 18.8889 | 114 | 1.1182 | 0.6667 |
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+ | 0.8852 | 19.8889 | 120 | 1.1026 | 0.6833 |
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+ | 0.8852 | 20.8889 | 126 | 1.0868 | 0.6333 |
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+ | 0.7881 | 21.8889 | 132 | 1.1674 | 0.6333 |
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+ | 0.7881 | 22.8889 | 138 | 1.1763 | 0.6667 |
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+ | 0.7913 | 23.8889 | 144 | 1.3433 | 0.6333 |
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+ | 0.7913 | 24.8889 | 150 | 1.1228 | 0.7 |
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+ | 0.667 | 25.8889 | 156 | 1.2828 | 0.6667 |
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+ | 0.667 | 26.8889 | 162 | 1.2373 | 0.6833 |
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+ | 0.6299 | 27.8889 | 168 | 1.2951 | 0.6667 |
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+ | 0.6299 | 28.8889 | 174 | 1.3410 | 0.6333 |
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+ | 0.5409 | 29.8889 | 180 | 1.1852 | 0.7 |
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+ | 0.5409 | 30.8889 | 186 | 1.4286 | 0.6167 |
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+ | 0.6085 | 31.8889 | 192 | 1.2376 | 0.65 |
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+ | 0.6085 | 32.8889 | 198 | 1.2249 | 0.6667 |
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+ | 0.562 | 33.8889 | 204 | 1.3640 | 0.6333 |
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+ | 0.562 | 34.8889 | 210 | 1.4234 | 0.6333 |
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+ | 0.4543 | 35.8889 | 216 | 1.3489 | 0.6333 |
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+ | 0.4543 | 36.8889 | 222 | 1.3273 | 0.6333 |
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+ | 0.4708 | 37.8889 | 228 | 1.3215 | 0.6333 |
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+ | 0.4708 | 38.8889 | 234 | 1.3277 | 0.6333 |
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+ | 0.5217 | 39.8889 | 240 | 1.3276 | 0.6333 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.1
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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