Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swin-tiny-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swin-tiny-patch4-window7-224-finetuned-vit
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8061420345489443
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# swin-tiny-patch4-window7-224-finetuned-vit
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.5516
+- Crack: {'precision': 0.575, 'recall': 0.71875, 'f1-score': 0.6388888888888888, 'support': 32}
+- Environment - ground: {'precision': 0.9714285714285714, 'recall': 0.9714285714285714, 'f1-score': 0.9714285714285714, 'support': 35}
+- Environment - other: {'precision': 0.8571428571428571, 'recall': 0.8888888888888888, 'f1-score': 0.8727272727272727, 'support': 27}
+- Environment - sky: {'precision': 0.9761904761904762, 'recall': 0.9318181818181818, 'f1-score': 0.9534883720930233, 'support': 44}
+- Environment - vegetation: {'precision': 0.9791666666666666, 'recall': 0.9791666666666666, 'f1-score': 0.9791666666666666, 'support': 48}
+- Joint defect: {'precision': 0.9166666666666666, 'recall': 0.7096774193548387, 'f1-score': 0.7999999999999999, 'support': 31}
+- Loss of section: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2}
+- Spalling: {'precision': 0.6041666666666666, 'recall': 0.6041666666666666, 'f1-score': 0.6041666666666666, 'support': 48}
+- Vegetation: {'precision': 0.8309859154929577, 'recall': 0.8939393939393939, 'f1-score': 0.8613138686131386, 'support': 66}
+- Wall - grafitti: {'precision': 0.7, 'recall': 0.9545454545454546, 'f1-score': 0.8076923076923077, 'support': 22}
+- Wall - normal: {'precision': 0.6976744186046512, 'recall': 0.7317073170731707, 'f1-score': 0.7142857142857143, 'support': 41}
+- Wall - other: {'precision': 0.7910447761194029, 'recall': 0.7794117647058824, 'f1-score': 0.7851851851851852, 'support': 68}
+- Wall - stain: {'precision': 0.8222222222222222, 'recall': 0.6491228070175439, 'f1-score': 0.7254901960784313, 'support': 57}
+- Accuracy: 0.8061
+- Macro avg: {'precision': 0.7478222490154723, 'recall': 0.754817164008097, 'f1-score': 0.7472179777173742, 'support': 521}
+- Weighted avg: {'precision': 0.8107856771401473, 'recall': 0.8061420345489443, 'f1-score': 0.8050072232872345, 'support': 521}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Crack                                                                                               | Environment - ground                                                                                           | Environment - other                                                                                            | Environment - sky                                                                                              | Environment - vegetation                                                                                       | Joint defect                                                                                                   | Loss of section                                                  | Spalling                                                                                                       | Vegetation                                                                                                     | Wall - grafitti                                                                                                | Wall - normal                                                                                                  | Wall - other                                                                                                   | Wall - stain                                                                                                   | Accuracy | Macro avg                                                                                                       | Weighted avg                                                                                                    |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|
+| 0.9193        | 1.0   | 146  | 0.7596          | {'precision': 0.5681818181818182, 'recall': 0.78125, 'f1-score': 0.6578947368421052, 'support': 32} | {'precision': 0.9444444444444444, 'recall': 0.9714285714285714, 'f1-score': 0.9577464788732395, 'support': 35} | {'precision': 0.8846153846153846, 'recall': 0.8518518518518519, 'f1-score': 0.8679245283018868, 'support': 27} | {'precision': 0.9736842105263158, 'recall': 0.8409090909090909, 'f1-score': 0.9024390243902439, 'support': 44} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 48}                                              | {'precision': 0.7419354838709677, 'recall': 0.7419354838709677, 'f1-score': 0.7419354838709677, 'support': 31} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2} | {'precision': 0.5769230769230769, 'recall': 0.3125, 'f1-score': 0.4054054054054054, 'support': 48}             | {'precision': 0.75, 'recall': 0.9090909090909091, 'f1-score': 0.821917808219178, 'support': 66}                | {'precision': 0.5142857142857142, 'recall': 0.8181818181818182, 'f1-score': 0.6315789473684209, 'support': 22} | {'precision': 0.7692307692307693, 'recall': 0.4878048780487805, 'f1-score': 0.5970149253731344, 'support': 41} | {'precision': 0.7540983606557377, 'recall': 0.6764705882352942, 'f1-score': 0.7131782945736433, 'support': 68} | {'precision': 0.6428571428571429, 'recall': 0.7894736842105263, 'f1-score': 0.7086614173228346, 'support': 57} | 0.7562   | {'precision': 0.7015581850454902, 'recall': 0.7062228366021391, 'f1-score': 0.692745926964697, 'support': 521}  | {'precision': 0.7618631381912654, 'recall': 0.7562380038387716, 'f1-score': 0.7479524876767193, 'support': 521} |
+| 0.7347        | 2.0   | 293  | 0.6495          | {'precision': 0.5526315789473685, 'recall': 0.65625, 'f1-score': 0.6, 'support': 32}                | {'precision': 1.0, 'recall': 0.9714285714285714, 'f1-score': 0.9855072463768115, 'support': 35}                | {'precision': 0.8461538461538461, 'recall': 0.8148148148148148, 'f1-score': 0.830188679245283, 'support': 27}  | {'precision': 0.9761904761904762, 'recall': 0.9318181818181818, 'f1-score': 0.9534883720930233, 'support': 44} | {'precision': 0.9591836734693877, 'recall': 0.9791666666666666, 'f1-score': 0.9690721649484536, 'support': 48} | {'precision': 0.9130434782608695, 'recall': 0.6774193548387096, 'f1-score': 0.7777777777777777, 'support': 31} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2} | {'precision': 0.5306122448979592, 'recall': 0.5416666666666666, 'f1-score': 0.5360824742268041, 'support': 48} | {'precision': 0.7058823529411765, 'recall': 0.9090909090909091, 'f1-score': 0.794701986754967, 'support': 66}  | {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22} | {'precision': 0.5510204081632653, 'recall': 0.6585365853658537, 'f1-score': 0.6, 'support': 41}                | {'precision': 0.8095238095238095, 'recall': 0.75, 'f1-score': 0.7786259541984734, 'support': 68}               | {'precision': 0.9393939393939394, 'recall': 0.543859649122807, 'f1-score': 0.688888888888889, 'support': 57}   | 0.7678   | {'precision': 0.7243822416365717, 'recall': 0.7152067510345803, 'f1-score': 0.7111617519445933, 'support': 521} | {'precision': 0.7869554245446998, 'recall': 0.7677543186180422, 'f1-score': 0.7672943491004631, 'support': 521} |
+| 0.7515        | 2.99  | 438  | 0.5516          | {'precision': 0.575, 'recall': 0.71875, 'f1-score': 0.6388888888888888, 'support': 32}              | {'precision': 0.9714285714285714, 'recall': 0.9714285714285714, 'f1-score': 0.9714285714285714, 'support': 35} | {'precision': 0.8571428571428571, 'recall': 0.8888888888888888, 'f1-score': 0.8727272727272727, 'support': 27} | {'precision': 0.9761904761904762, 'recall': 0.9318181818181818, 'f1-score': 0.9534883720930233, 'support': 44} | {'precision': 0.9791666666666666, 'recall': 0.9791666666666666, 'f1-score': 0.9791666666666666, 'support': 48} | {'precision': 0.9166666666666666, 'recall': 0.7096774193548387, 'f1-score': 0.7999999999999999, 'support': 31} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2} | {'precision': 0.6041666666666666, 'recall': 0.6041666666666666, 'f1-score': 0.6041666666666666, 'support': 48} | {'precision': 0.8309859154929577, 'recall': 0.8939393939393939, 'f1-score': 0.8613138686131386, 'support': 66} | {'precision': 0.7, 'recall': 0.9545454545454546, 'f1-score': 0.8076923076923077, 'support': 22}                | {'precision': 0.6976744186046512, 'recall': 0.7317073170731707, 'f1-score': 0.7142857142857143, 'support': 41} | {'precision': 0.7910447761194029, 'recall': 0.7794117647058824, 'f1-score': 0.7851851851851852, 'support': 68} | {'precision': 0.8222222222222222, 'recall': 0.6491228070175439, 'f1-score': 0.7254901960784313, 'support': 57} | 0.8061   | {'precision': 0.7478222490154723, 'recall': 0.754817164008097, 'f1-score': 0.7472179777173742, 'support': 521}  | {'precision': 0.8107856771401473, 'recall': 0.8061420345489443, 'f1-score': 0.8050072232872345, 'support': 521} |
+
+
+### Framework versions
+
+- Transformers 4.33.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3