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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: swinv2-tiny-patch4-window8-256-RD-aptos19
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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: 0.6739130434782609
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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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# swinv2-tiny-patch4-window8-256-RD-aptos19
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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: 144573075075950992480149202324684800.0000
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- Accuracy: 0.6739
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.00015
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 40
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-----------------------------------------:|:-----:|:----:|:-----------------------------------------:|:--------:|
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| No log | 0.86 | 3 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| No log | 2.0 | 7 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 141735823463928302525633790371430400.0000 | 2.86 | 10 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 141735823463928302525633790371430400.0000 | 4.0 | 14 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 141735823463928302525633790371430400.0000 | 4.86 | 17 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 148386187888478135085935683952443392.0000 | 6.0 | 21 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 148386187888478135085935683952443392.0000 | 6.86 | 24 | 144573075075950992480149202324684800.0000 | 0.4783 |
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| 148386187888478135085935683952443392.0000 | 8.0 | 28 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 166674646480500797315403436963921920.0000 | 8.86 | 31 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 166674646480500797315403436963921920.0000 | 10.0 | 35 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 166674646480500797315403436963921920.0000 | 10.86 | 38 | 144573075075950992480149202324684800.0000 | 0.4565 |
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| 123031678471642034838718731348082688.0000 | 12.0 | 42 | 144573075075950992480149202324684800.0000 | 0.5217 |
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| 123031678471642034838718731348082688.0000 | 12.86 | 45 | 144573075075950992480149202324684800.0000 | 0.6087 |
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| 123031678471642034838718731348082688.0000 | 14.0 | 49 | 144573075075950992480149202324684800.0000 | 0.5435 |
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| 160439944687765812243898756589682688.0000 | 14.86 | 52 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 160439944687765812243898756589682688.0000 | 16.0 | 56 | 144573075075950992480149202324684800.0000 | 0.5870 |
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| 160439944687765812243898756589682688.0000 | 16.86 | 59 | 144573075075950992480149202324684800.0000 | 0.5652 |
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| 151295747083019479456202288017702912.0000 | 18.0 | 63 | 144573075075950992480149202324684800.0000 | 0.6087 |
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| 151295747083019479456202288017702912.0000 | 18.86 | 66 | 144573075075950992480149202324684800.0000 | 0.6304 |
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| 142151454404478133240649521934893056.0000 | 20.0 | 70 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 142151454404478133240649521934893056.0000 | 20.86 | 73 | 144573075075950992480149202324684800.0000 | 0.6739 |
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| 142151454404478133240649521934893056.0000 | 22.0 | 77 | 144573075075950992480149202324684800.0000 | 0.6739 |
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| 137163724661555136131785556085440512.0000 | 22.86 | 80 | 144573075075950992480149202324684800.0000 | 0.6304 |
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| 137163724661555136131785556085440512.0000 | 24.0 | 84 | 144573075075950992480149202324684800.0000 | 0.6304 |
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| 137163724661555136131785556085440512.0000 | 24.86 | 87 | 144573075075950992480149202324684800.0000 | 0.6739 |
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| 137163692970290119358004074442129408.0000 | 26.0 | 91 | 144573075075950992480149202324684800.0000 | 0.6304 |
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| 137163692970290119358004074442129408.0000 | 26.86 | 94 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 137163692970290119358004074442129408.0000 | 28.0 | 98 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 155452183253577798361253309096919040.0000 | 28.86 | 101 | 144573075075950992480149202324684800.0000 | 0.6739 |
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| 155452183253577798361253309096919040.0000 | 30.0 | 105 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 155452183253577798361253309096919040.0000 | 30.86 | 108 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 139657557841751617912436057366855680.0000 | 32.0 | 112 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 139657557841751617912436057366855680.0000 | 32.86 | 115 | 144573075075950992480149202324684800.0000 | 0.6522 |
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| 139657557841751617912436057366855680.0000 | 34.0 | 119 | 144573075075950992480149202324684800.0000 | 0.6304 |
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| 141735791772663285751852308728119296.0000 | 34.29 | 120 | 144573075075950992480149202324684800.0000 | 0.6304 |
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### Framework versions
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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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