Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: vit-base-patch16-224-in21k-crack-detectorVITmain50epochs
+  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:
+        accuracy: 0.9845817447858264
+    - name: F1
+      type: f1
+      value:
+        f1: 0.983869840492846
+    - name: Precision
+      type: precision
+      value:
+        precision: 0.9840346899184906
+    - name: Recall
+      type: recall
+      value:
+        recall: 0.9837409101507315
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-in21k-crack-detectorVITmain50epochs
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0670
+- Accuracy: {'accuracy': 0.9845817447858264}
+- F1: {'f1': 0.983869840492846}
+- Precision: {'precision': 0.9840346899184906}
+- Recall: {'recall': 0.9837409101507315}
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
+|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
+| 0.5622        | 1.0   | 1114  | 0.4109          | {'accuracy': 0.8736263736263736} | {'f1': 0.8653360894930927} | {'precision': 0.8669392826942903} | {'recall': 0.8641606609285752} |
+| 0.3696        | 2.0   | 2229  | 0.2493          | {'accuracy': 0.9249271137026239} | {'f1': 0.9207602119769538} | {'precision': 0.9199949527238288} | {'recall': 0.9219224853720074} |
+| 0.321         | 3.0   | 3344  | 0.1849          | {'accuracy': 0.9410742318905584} | {'f1': 0.9378351696354423} | {'precision': 0.9376663800198108} | {'recall': 0.9381803360170522} |
+| 0.3403        | 4.0   | 4459  | 0.1828          | {'accuracy': 0.9382148463781117} | {'f1': 0.9358183038090213} | {'precision': 0.9351989305945833} | {'recall': 0.9374254756058774} |
+| 0.2399        | 5.0   | 5573  | 0.1283          | {'accuracy': 0.9539694998878673} | {'f1': 0.9516195088904471} | {'precision': 0.9525864748624857} | {'recall': 0.9506985041313745} |
+| 0.2241        | 6.0   | 6688  | 0.1133          | {'accuracy': 0.9605292666517157} | {'f1': 0.9584812990028835} | {'precision': 0.9604748305069212} | {'recall': 0.9567171466267748} |
+| 0.2375        | 7.0   | 7803  | 0.1138          | {'accuracy': 0.9638932496075353} | {'f1': 0.9615520166695122} | {'precision': 0.9628392910079963} | {'recall': 0.9605232709871921} |
+| 0.1946        | 8.0   | 8918  | 0.0935          | {'accuracy': 0.9682103610675039} | {'f1': 0.9665726403969513} | {'precision': 0.9677788948564787} | {'recall': 0.9654971437363139} |
+| 0.1513        | 9.0   | 10032 | 0.0908          | {'accuracy': 0.967593630858937}  | {'f1': 0.9657913168767667} | {'precision': 0.9657840712769861} | {'recall': 0.9659100270116727} |
+| 0.174         | 10.0  | 11147 | 0.0851          | {'accuracy': 0.968490692980489}  | {'f1': 0.9669266120574112} | {'precision': 0.9663934578407292} | {'recall': 0.967857117571674}  |
+| 0.1376        | 11.0  | 12262 | 0.0848          | {'accuracy': 0.9692756223368468} | {'f1': 0.9676560659554989} | {'precision': 0.9695619317426214} | {'recall': 0.9663476308224139} |
+| 0.1681        | 12.0  | 13377 | 0.0789          | {'accuracy': 0.9743776631531733} | {'f1': 0.9731609997444961} | {'precision': 0.973142077383281}  | {'recall': 0.9733127731609976} |
+| 0.0998        | 13.0  | 14491 | 0.0750          | {'accuracy': 0.9749943933617403} | {'f1': 0.9737724540493713} | {'precision': 0.9739505389554447} | {'recall': 0.9737043141281871} |
+| 0.0968        | 14.0  | 15606 | 0.0837          | {'accuracy': 0.9737609329446064} | {'f1': 0.9722837146803432} | {'precision': 0.9729901044455692} | {'recall': 0.9716272189580044} |
+| 0.0841        | 15.0  | 16721 | 0.0689          | {'accuracy': 0.9758914554832923} | {'f1': 0.974522946671967}  | {'precision': 0.9749603470712005} | {'recall': 0.974152667413215}  |
+| 0.095         | 16.0  | 17836 | 0.0705          | {'accuracy': 0.9762278537788742} | {'f1': 0.9749967013469043} | {'precision': 0.9757537211246345} | {'recall': 0.9743092871293193} |
+| 0.1055        | 17.0  | 18950 | 0.0709          | {'accuracy': 0.9753307916573223} | {'f1': 0.9739943140941046} | {'precision': 0.974174353159638}  | {'recall': 0.9738453333404872} |
+| 0.0644        | 18.0  | 20065 | 0.0738          | {'accuracy': 0.9766763848396501} | {'f1': 0.9755727076296268} | {'precision': 0.9750313848950637} | {'recall': 0.9764061086777133} |
+| 0.0839        | 19.0  | 21180 | 0.0691          | {'accuracy': 0.9770127831352321} | {'f1': 0.9758154254345139} | {'precision': 0.9761822643915987} | {'recall': 0.9754580155091578} |
+| 0.0752        | 20.0  | 22295 | 0.0737          | {'accuracy': 0.9765642520744562} | {'f1': 0.9753527993174993} | {'precision': 0.9756389600577889} | {'recall': 0.9751662665128649} |
+| 0.0777        | 21.0  | 23409 | 0.0594          | {'accuracy': 0.9806010316214397} | {'f1': 0.9797190165517261} | {'precision': 0.9797694892510824} | {'recall': 0.9796726937896714} |
+| 0.0753        | 22.0  | 24524 | 0.0673          | {'accuracy': 0.9798721686476789} | {'f1': 0.9791539833000008} | {'precision': 0.9788167338624194} | {'recall': 0.9796378458227846} |
+| 0.0564        | 23.0  | 25639 | 0.0670          | {'accuracy': 0.9795357703520969} | {'f1': 0.9785354369309488} | {'precision': 0.9785832048371994} | {'recall': 0.9785660480900121} |
+| 0.0721        | 24.0  | 26754 | 0.0685          | {'accuracy': 0.9800964341780668} | {'f1': 0.9791442297494849} | {'precision': 0.9800176266790614} | {'recall': 0.9783165451081763} |
+| 0.0637        | 25.0  | 27868 | 0.0683          | {'accuracy': 0.9804888988562458} | {'f1': 0.9793615860016096} | {'precision': 0.9795645667425841} | {'recall': 0.9792228423789527} |
+| 0.0845        | 26.0  | 28983 | 0.0687          | {'accuracy': 0.9789190401435299} | {'f1': 0.9779974196750578} | {'precision': 0.9777924373303344} | {'recall': 0.9782512107344294} |
+| 0.0443        | 27.0  | 30098 | 0.0660          | {'accuracy': 0.9821148239515587} | {'f1': 0.9812042927695707} | {'precision': 0.9814603709525461} | {'recall': 0.9810002705393822} |
+| 0.0544        | 28.0  | 31213 | 0.0778          | {'accuracy': 0.9791993720565149} | {'f1': 0.9782906130895705} | {'precision': 0.9787040955747366} | {'recall': 0.9779528847328706} |
+| 0.0537        | 29.0  | 32327 | 0.0674          | {'accuracy': 0.9818344920385736} | {'f1': 0.9811984625176332} | {'precision': 0.9815390282130447} | {'recall': 0.9809323144878648} |
+| 0.0493        | 30.0  | 33442 | 0.0701          | {'accuracy': 0.9814980937429917} | {'f1': 0.9805602239528695} | {'precision': 0.9811340066266928} | {'recall': 0.9800627824611866} |
+| 0.0522        | 31.0  | 34557 | 0.0710          | {'accuracy': 0.9814980937429917} | {'f1': 0.9808148178357844} | {'precision': 0.9806080192879051} | {'recall': 0.9810839837288577} |
+| 0.0159        | 32.0  | 35672 | 0.0724          | {'accuracy': 0.9820026911863646} | {'f1': 0.981223779316131}  | {'precision': 0.9815852582883647} | {'recall': 0.9809265201908598} |
+| 0.0469        | 33.0  | 36786 | 0.0681          | {'accuracy': 0.9827315541601256} | {'f1': 0.982079515043356}  | {'precision': 0.9821689199854957} | {'recall': 0.9820640757052289} |
+| 0.0469        | 34.0  | 37901 | 0.0678          | {'accuracy': 0.9817223592733797} | {'f1': 0.9809328125490557} | {'precision': 0.9810701495429422} | {'recall': 0.9808658173842284} |
+| 0.0385        | 35.0  | 39016 | 0.0634          | {'accuracy': 0.9842453464902444} | {'f1': 0.9836492800003742} | {'precision': 0.9834341260971065} | {'recall': 0.9838961344763829} |
+| 0.0386        | 36.0  | 40131 | 0.0687          | {'accuracy': 0.9839089481946625} | {'f1': 0.983239373896663}  | {'precision': 0.983372610840443}  | {'recall': 0.9831603310143421} |
+| 0.031         | 37.0  | 41245 | 0.0643          | {'accuracy': 0.9837968154294685} | {'f1': 0.9830873488066041} | {'precision': 0.983437856778449}  | {'recall': 0.982767386441964}  |
+| 0.0188        | 38.0  | 42360 | 0.0672          | {'accuracy': 0.9840771473424534} | {'f1': 0.9834705260789471} | {'precision': 0.9841321671664716} | {'recall': 0.9828412386527916} |
+| 0.0406        | 39.0  | 43475 | 0.0575          | {'accuracy': 0.9851424086117964} | {'f1': 0.9846872838275066} | {'precision': 0.9845262374841439} | {'recall': 0.9848621233267102} |
+| 0.0725        | 40.0  | 44590 | 0.0654          | {'accuracy': 0.9843574792554385} | {'f1': 0.9836921118378804} | {'precision': 0.9838071847209022} | {'recall': 0.9836248084545489} |
+| 0.0456        | 41.0  | 45704 | 0.0652          | {'accuracy': 0.9837407490468715} | {'f1': 0.9831247046792615} | {'precision': 0.9830251647671387} | {'recall': 0.9832768782292008} |
+| 0.0272        | 42.0  | 46819 | 0.0670          | {'accuracy': 0.9835725498990805} | {'f1': 0.9828974576921549} | {'precision': 0.9829831457743452} | {'recall': 0.9828424029347932} |
+| 0.021         | 43.0  | 47934 | 0.0633          | {'accuracy': 0.9847499439336174} | {'f1': 0.9841868836767669} | {'precision': 0.9842200567065219} | {'recall': 0.9841697876663609} |
+| 0.0231        | 44.0  | 49049 | 0.0724          | {'accuracy': 0.9825072886297376} | {'f1': 0.9819634359798524} | {'precision': 0.9814536283421093} | {'recall': 0.9825728366747504} |
+| 0.0419        | 45.0  | 50163 | 0.0652          | {'accuracy': 0.9844135456380354} | {'f1': 0.9837880332204787} | {'precision': 0.9837369807084215} | {'recall': 0.98386986331642}   |
+| 0.0257        | 46.0  | 51278 | 0.0673          | {'accuracy': 0.9842453464902444} | {'f1': 0.98363705023352}   | {'precision': 0.9835068164678353} | {'recall': 0.983839536345849}  |
+| 0.0238        | 47.0  | 52393 | 0.0670          | {'accuracy': 0.9850302758466024} | {'f1': 0.9844204182477418} | {'precision': 0.9846537574771261} | {'recall': 0.9842341338822049} |
+| 0.0185        | 48.0  | 53508 | 0.0675          | {'accuracy': 0.9844696120206324} | {'f1': 0.9837444887203743} | {'precision': 0.9840638645916341} | {'recall': 0.983456407378361}  |
+| 0.0196        | 49.0  | 54622 | 0.0664          | {'accuracy': 0.9844135456380354} | {'f1': 0.9837492030151856} | {'precision': 0.9839059161676366} | {'recall': 0.9836254408912738} |
+| 0.0352        | 49.97 | 55700 | 0.0670          | {'accuracy': 0.9845817447858264} | {'f1': 0.983869840492846}  | {'precision': 0.9840346899184906} | {'recall': 0.9837409101507315} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0
+- Datasets 2.17.1
+- Tokenizers 0.15.2