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vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochsTrainValAUGMENTED
mmomm25/vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochsTrainValAUGMENTED
vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochsTrainValAUGMENTED is a machine learning model from mmomm25. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Oct 1, 2024
- Updated
- Oct 1, 2024
- SHA
- 66b259a4
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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-FINALAsphaltLaneClassifier-detectorVIT30epochsTrainValAUGMENTED
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: 0.0427
- Accuracy: {'accuracy': 0.9804996953077392}
- F1: {'f1': 0.9800980973913306}
- Precision: {'precision': 0.9820258378580791}
- Recall: {'recall': 0.9807653776798236}
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2763 | 0.9994 | 1230 | 0.2269 | {'accuracy': 0.9640463132236441} | {'f1': 0.9630811116832685} | {'precision': 0.9640657360845907} | {'recall': 0.964963663987968} |
| 0.2271 | 1.9996 | 2461 | 0.0938 | {'accuracy': 0.9764371318301849} | {'f1': 0.9761448410653392} | {'precision': 0.977818779128281} | {'recall': 0.9770355856317611} |
| 0.1737 | 2.9998 | 3692 | 0.1073 | {'accuracy': 0.9577493398334349} | {'f1': 0.9569000292933705} | {'precision': 0.959800239637741} | {'recall': 0.9552400643800896} |
| 0.0361 | 4.0 | 4923 | 0.0532 | {'accuracy': 0.9782652853950843} | {'f1': 0.9778377521587307} | {'precision': 0.9794461626122134} | {'recall': 0.9785602619414852} |
| 0.1613 | 4.9994 | 6153 | 0.0574 | {'accuracy': 0.9762340036563071} | {'f1': 0.9759632908989441} | {'precision': 0.9776775737495528} | {'recall': 0.9768318271751163} |
| 0.098 | 5.9996 | 7384 | 0.0551 | {'accuracy': 0.9774527726995734} | {'f1': 0.9771202466632837} | {'precision': 0.9788281272701719} | {'recall': 0.9779919889728328} |
| 0.0867 | 6.9998 | 8615 | 0.0476 | {'accuracy': 0.9794840544383506} | {'f1': 0.9791014077010869} | {'precision': 0.9810276957543254} | {'recall': 0.9798251744941444} |
| 0.0855 | 8.0 | 9846 | 0.0657 | {'accuracy': 0.9731870810481413} | {'f1': 0.9729473784067677} | {'precision': 0.9755572390111291} | {'recall': 0.973170601861673} |
| 0.1041 | 8.9994 | 11076 | 0.0454 | {'accuracy': 0.9802965671338615} | {'f1': 0.9799080834672663} | {'precision': 0.981846143338762} | {'recall': 0.9805652976478109} |
| 0.0674 | 9.9996 | 12307 | 0.0471 | {'accuracy': 0.9794840544383506} | {'f1': 0.9791012017629246} | {'precision': 0.9809386449900642} | {'recall': 0.9798657735056604} |
| 0.1083 | 10.9998 | 13538 | 0.0437 | {'accuracy': 0.9802965671338615} | {'f1': 0.9799080834672663} | {'precision': 0.981846143338762} | {'recall': 0.9805652976478109} |
| 0.1197 | 12.0 | 14769 | 0.0560 | {'accuracy': 0.9766402600040626} | {'f1': 0.9763088751876786} | {'precision': 0.9779331362017656} | {'recall': 0.9773126240278623} |
| 0.0543 | 12.9994 | 15999 | 0.0455 | {'accuracy': 0.9794840544383506} | {'f1': 0.9791012017629246} | {'precision': 0.9809386449900642} | {'recall': 0.9798657735056604} |
| 0.0949 | 13.9996 | 17230 | 0.0440 | {'accuracy': 0.9802965671338615} | {'f1': 0.9798985564872046} | {'precision': 0.981846143338762} | {'recall': 0.9805462716324967} |
| 0.0764 | 14.9998 | 18461 | 0.0508 | {'accuracy': 0.9782652853950843} | {'f1': 0.9779293131150244} | {'precision': 0.9797174734398995} | {'recall': 0.9787862484966643} |
| 0.0482 | 16.0 | 19692 | 0.0625 | {'accuracy': 0.9760308754824294} | {'f1': 0.9757209855091478} | {'precision': 0.9773628450645225} | {'recall': 0.9768071193135048} |
| 0.0504 | 16.9994 | 20922 | 0.0428 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.1044 | 17.9996 | 22153 | 0.0428 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.07 | 18.9998 | 23384 | 0.0431 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.0804 | 20.0 | 24615 | 0.0439 | {'accuracy': 0.9802965671338615} | {'f1': 0.9798986112130946} | {'precision': 0.9818070673483438} | {'recall': 0.980585456844991} |
| 0.0334 | 20.9994 | 25845 | 0.0443 | {'accuracy': 0.9800934389599838} | {'f1': 0.9796902317752838} | {'precision': 0.9816436079495018} | {'recall': 0.9803850961958226} |
| 0.0839 | 21.9996 | 27076 | 0.0433 | {'accuracy': 0.9802965671338615} | {'f1': 0.9798986112130946} | {'precision': 0.9818070673483438} | {'recall': 0.980585456844991} |
| 0.0826 | 22.9998 | 28307 | 0.0443 | {'accuracy': 0.9802965671338615} | {'f1': 0.9798918326004827} | {'precision': 0.98177108929345} | {'recall': 0.9805511990548504} |
| 0.0897 | 24.0 | 29538 | 0.0434 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.0911 | 24.9994 | 30768 | 0.0437 | {'accuracy': 0.9800934389599838} | {'f1': 0.9796991791604638} | {'precision': 0.9815889658615741} | {'recall': 0.9804055360101583} |
| 0.0266 | 25.9996 | 31999 | 0.0426 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.0453 | 26.9998 | 33230 | 0.0453 | {'accuracy': 0.9796871826122283} | {'f1': 0.9793004750340752} | {'precision': 0.9811547577187063} | {'recall': 0.980045694340493} |
| 0.0509 | 28.0 | 34461 | 0.0427 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.0682 | 28.9994 | 35691 | 0.0429 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
| 0.0601 | 29.9817 | 36900 | 0.0427 | {'accuracy': 0.9804996953077392} | {'f1': 0.9800980973913306} | {'precision': 0.9820258378580791} | {'recall': 0.9807653776798236} |
Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
- Type
- vit
- License
- apache-2.0
- Created
- Oct 1, 2024
- Updated
- Oct 1, 2024