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--- |
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license: apache-2.0 |
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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: delivery_truck_classification |
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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: train |
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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: 0.9491525423728814 |
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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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# delivery_truck_classification |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1253 |
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- Accuracy: 0.9492 |
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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: 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: 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 | 0.94 | 4 | 1.8882 | 0.1186 | |
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| No log | 1.94 | 8 | 1.6799 | 0.3559 | |
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| No log | 2.94 | 12 | 1.4260 | 0.5763 | |
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| No log | 3.94 | 16 | 1.1092 | 0.6780 | |
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| 1.7242 | 4.94 | 20 | 0.8653 | 0.7458 | |
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| 1.7242 | 5.94 | 24 | 0.6787 | 0.7797 | |
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| 1.7242 | 6.94 | 28 | 0.5506 | 0.8305 | |
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| 1.7242 | 7.94 | 32 | 0.4174 | 0.8814 | |
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| 1.7242 | 8.94 | 36 | 0.3643 | 0.8814 | |
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| 0.8337 | 9.94 | 40 | 0.2680 | 0.9322 | |
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| 0.8337 | 10.94 | 44 | 0.2705 | 0.8983 | |
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| 0.8337 | 11.94 | 48 | 0.2270 | 0.9153 | |
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| 0.8337 | 12.94 | 52 | 0.1790 | 0.9492 | |
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| 0.8337 | 13.94 | 56 | 0.1694 | 0.9322 | |
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| 0.493 | 14.94 | 60 | 0.1776 | 0.9153 | |
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| 0.493 | 15.94 | 64 | 0.1831 | 0.9322 | |
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| 0.493 | 16.94 | 68 | 0.1765 | 0.9322 | |
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| 0.493 | 17.94 | 72 | 0.1575 | 0.9322 | |
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| 0.493 | 18.94 | 76 | 0.1472 | 0.9322 | |
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| 0.3966 | 19.94 | 80 | 0.1360 | 0.9322 | |
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| 0.3966 | 20.94 | 84 | 0.1448 | 0.9492 | |
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| 0.3966 | 21.94 | 88 | 0.1658 | 0.9322 | |
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| 0.3966 | 22.94 | 92 | 0.1652 | 0.9322 | |
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| 0.3966 | 23.94 | 96 | 0.1565 | 0.9322 | |
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| 0.3645 | 24.94 | 100 | 0.1701 | 0.9322 | |
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| 0.3645 | 25.94 | 104 | 0.1830 | 0.9322 | |
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| 0.3645 | 26.94 | 108 | 0.1682 | 0.9322 | |
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| 0.3645 | 27.94 | 112 | 0.1410 | 0.9492 | |
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| 0.3645 | 28.94 | 116 | 0.1291 | 0.9492 | |
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| 0.3358 | 29.94 | 120 | 0.1248 | 0.9492 | |
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| 0.3358 | 30.94 | 124 | 0.1275 | 0.9492 | |
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| 0.3358 | 31.94 | 128 | 0.1257 | 0.9492 | |
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| 0.3358 | 32.94 | 132 | 0.1288 | 0.9492 | |
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| 0.3358 | 33.94 | 136 | 0.1246 | 0.9492 | |
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| 0.3049 | 34.94 | 140 | 0.1219 | 0.9492 | |
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| 0.3049 | 35.94 | 144 | 0.1224 | 0.9492 | |
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| 0.3049 | 36.94 | 148 | 0.1246 | 0.9492 | |
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| 0.3049 | 37.94 | 152 | 0.1243 | 0.9492 | |
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| 0.3049 | 38.94 | 156 | 0.1248 | 0.9492 | |
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| 0.2962 | 39.94 | 160 | 0.1253 | 0.9492 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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