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SW2-DMAE-2

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swinv2
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  2. model.safetensors +1 -1
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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-2
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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: 0.6521739130434783
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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-2
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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: 0.9880
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+ - Accuracy: 0.6522
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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: 1.5e-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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+ - num_epochs: 80
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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 | 0.86 | 3 | 1.6269 | 0.1087 |
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+ | No log | 2.0 | 7 | 1.6078 | 0.1087 |
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+ | 1.618 | 2.86 | 10 | 1.5852 | 0.1087 |
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+ | 1.618 | 4.0 | 14 | 1.5398 | 0.1087 |
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+ | 1.618 | 4.86 | 17 | 1.4948 | 0.1087 |
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+ | 1.5162 | 6.0 | 21 | 1.4344 | 0.1087 |
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+ | 1.5162 | 6.86 | 24 | 1.3879 | 0.1087 |
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+ | 1.5162 | 8.0 | 28 | 1.3288 | 0.1739 |
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+ | 1.3459 | 8.86 | 31 | 1.2926 | 0.4565 |
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+ | 1.3459 | 10.0 | 35 | 1.2562 | 0.4565 |
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+ | 1.3459 | 10.86 | 38 | 1.2384 | 0.4565 |
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+ | 1.2384 | 12.0 | 42 | 1.2205 | 0.4565 |
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+ | 1.2384 | 12.86 | 45 | 1.2174 | 0.4565 |
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+ | 1.2384 | 14.0 | 49 | 1.2131 | 0.4565 |
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+ | 1.2049 | 14.86 | 52 | 1.2104 | 0.4565 |
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+ | 1.2049 | 16.0 | 56 | 1.2086 | 0.4565 |
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+ | 1.2049 | 16.86 | 59 | 1.2076 | 0.4565 |
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+ | 1.1815 | 18.0 | 63 | 1.2052 | 0.4565 |
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+ | 1.1815 | 18.86 | 66 | 1.2049 | 0.4565 |
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+ | 1.1826 | 20.0 | 70 | 1.2019 | 0.4565 |
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+ | 1.1826 | 20.86 | 73 | 1.1960 | 0.4565 |
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+ | 1.1826 | 22.0 | 77 | 1.1927 | 0.4565 |
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+ | 1.1647 | 22.86 | 80 | 1.1928 | 0.4565 |
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+ | 1.1647 | 24.0 | 84 | 1.1924 | 0.4565 |
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+ | 1.1647 | 24.86 | 87 | 1.1903 | 0.4565 |
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+ | 1.1568 | 26.0 | 91 | 1.1879 | 0.4565 |
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+ | 1.1568 | 26.86 | 94 | 1.1913 | 0.4565 |
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+ | 1.1568 | 28.0 | 98 | 1.2046 | 0.4783 |
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+ | 1.1432 | 28.86 | 101 | 1.1934 | 0.4783 |
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+ | 1.1432 | 30.0 | 105 | 1.1665 | 0.4783 |
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+ | 1.1432 | 30.86 | 108 | 1.1601 | 0.4783 |
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+ | 1.1112 | 32.0 | 112 | 1.1624 | 0.5 |
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+ | 1.1112 | 32.86 | 115 | 1.1664 | 0.5217 |
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+ | 1.1112 | 34.0 | 119 | 1.1692 | 0.5 |
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+ | 1.1132 | 34.86 | 122 | 1.1513 | 0.5435 |
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+ | 1.1132 | 36.0 | 126 | 1.1384 | 0.5870 |
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+ | 1.1132 | 36.86 | 129 | 1.1274 | 0.6087 |
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+ | 1.0642 | 38.0 | 133 | 1.1443 | 0.5870 |
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+ | 1.0642 | 38.86 | 136 | 1.1651 | 0.5 |
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+ | 1.0439 | 40.0 | 140 | 1.1493 | 0.5 |
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+ | 1.0439 | 40.86 | 143 | 1.1331 | 0.5217 |
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+ | 1.0439 | 42.0 | 147 | 1.1032 | 0.5870 |
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+ | 1.0362 | 42.86 | 150 | 1.0988 | 0.6304 |
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+ | 1.0362 | 44.0 | 154 | 1.1093 | 0.5870 |
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+ | 1.0362 | 44.86 | 157 | 1.1101 | 0.5870 |
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+ | 1.0177 | 46.0 | 161 | 1.0903 | 0.6304 |
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+ | 1.0177 | 46.86 | 164 | 1.0691 | 0.6522 |
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+ | 1.0177 | 48.0 | 168 | 1.0510 | 0.6739 |
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+ | 1.0 | 48.86 | 171 | 1.0451 | 0.6522 |
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+ | 1.0 | 50.0 | 175 | 1.0425 | 0.6522 |
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+ | 1.0 | 50.86 | 178 | 1.0512 | 0.6087 |
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+ | 0.9636 | 52.0 | 182 | 1.0441 | 0.6304 |
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+ | 0.9636 | 52.86 | 185 | 1.0402 | 0.6522 |
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+ | 0.9636 | 54.0 | 189 | 1.0161 | 0.6522 |
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+ | 0.9744 | 54.86 | 192 | 1.0073 | 0.6522 |
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+ | 0.9744 | 56.0 | 196 | 1.0048 | 0.6522 |
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+ | 0.9744 | 56.86 | 199 | 0.9993 | 0.6522 |
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+ | 0.9233 | 58.0 | 203 | 0.9939 | 0.6522 |
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+ | 0.9233 | 58.86 | 206 | 0.9939 | 0.6522 |
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+ | 0.9452 | 60.0 | 210 | 0.9975 | 0.6522 |
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+ | 0.9452 | 60.86 | 213 | 0.9981 | 0.6087 |
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+ | 0.9452 | 62.0 | 217 | 0.9985 | 0.6087 |
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+ | 0.9183 | 62.86 | 220 | 0.9969 | 0.6087 |
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+ | 0.9183 | 64.0 | 224 | 0.9958 | 0.6304 |
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+ | 0.9183 | 64.86 | 227 | 0.9928 | 0.6087 |
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+ | 0.9449 | 66.0 | 231 | 0.9906 | 0.6087 |
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+ | 0.9449 | 66.86 | 234 | 0.9893 | 0.6304 |
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+ | 0.9449 | 68.0 | 238 | 0.9881 | 0.6522 |
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+ | 0.9154 | 68.57 | 240 | 0.9880 | 0.6522 |
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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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