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delivery_truck_classification

Image Classification
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update model card README.md

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@@ -4,9 +4,24 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
 
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  model-index:
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  - name: delivery_truck_classification
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -16,13 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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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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- - eval_loss: 0.1655
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- - eval_accuracy: 0.9492
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- - eval_runtime: 3.0476
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- - eval_samples_per_second: 19.36
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- - eval_steps_per_second: 0.656
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- - epoch: 0.94
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- - step: 4
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  ## Model description
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@@ -52,9 +62,55 @@ The following hyperparameters were used during training:
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 40
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  ### Framework versions
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  - Transformers 4.25.1
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- - Pytorch 1.13.1+cu117
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  - Datasets 2.8.0
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  - Tokenizers 0.13.2
 
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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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  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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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 40
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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 | 0.94 | 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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+
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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