metadata
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
vit-base-patch16-224-in21k-crack-detectorVITmain50epochs
This model is a fine-tuned version of 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