vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochsTrainVal
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: 0.0886
- Accuracy: {'accuracy': 0.9563318777292577}
- F1: {'f1': 0.9318274318274318}
- Precision: {'precision': 0.9367965367965368}
- Recall: {'recall': 0.927993839045052}
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 |
|---|---|---|---|---|---|---|---|
| 1.7198 | 0.9956 | 57 | 1.5576 | {'accuracy': 0.7467248908296943} | {'f1': 0.49276057276057283} | {'precision': 0.5726499690785405} | {'recall': 0.5304274269791511} |
| 0.9081 | 1.9913 | 114 | 0.7542 | {'accuracy': 0.9126637554585153} | {'f1': 0.6815669614826331} | {'precision': 0.6667189132706375} | {'recall': 0.700220596772321} |
| 0.4761 | 2.9869 | 171 | 0.3690 | {'accuracy': 0.9606986899563319} | {'f1': 0.8055100468538725} | {'precision': 0.8327510665283063} | {'recall': 0.7959183673469388} |
| 0.4783 | 4.0 | 229 | 0.3196 | {'accuracy': 0.9344978165938864} | {'f1': 0.7876659055230484} | {'precision': 0.8222068488412314} | {'recall': 0.7732035342872782} |
| 0.3541 | 4.9956 | 286 | 0.2280 | {'accuracy': 0.9563318777292577} | {'f1': 0.8029204504768414} | {'precision': 0.8327627953577691} | {'recall': 0.7909922589725545} |
| 0.3096 | 5.9913 | 343 | 0.1897 | {'accuracy': 0.9737991266375546} | {'f1': 0.9533858998144712} | {'precision': 0.9854721549636805} | {'recall': 0.9387755102040816} |
| 0.4737 | 6.9869 | 400 | 0.1658 | {'accuracy': 0.9781659388646288} | {'f1': 0.9625090929438755} | {'precision': 0.9876847290640394} | {'recall': 0.9489795918367347} |
| 0.2629 | 8.0 | 458 | 0.1555 | {'accuracy': 0.9606986899563319} | {'f1': 0.9470988692620754} | {'precision': 0.9443563789152024} | {'recall': 0.9509845577788695} |
| 0.2431 | 8.9956 | 515 | 0.1322 | {'accuracy': 0.9694323144104804} | {'f1': 0.9613275613275613} | {'precision': 0.9523809523809524} | {'recall': 0.9811320754716981} |
| 0.2829 | 9.9913 | 572 | 0.1333 | {'accuracy': 0.9694323144104804} | {'f1': 0.9613275613275613} | {'precision': 0.9523809523809524} | {'recall': 0.9811320754716981} |
| 0.2283 | 10.9869 | 629 | 0.1119 | {'accuracy': 0.9563318777292577} | {'f1': 0.9413515406162466} | {'precision': 0.9356980887593132} | {'recall': 0.9505198305737389} |
| 0.1808 | 12.0 | 687 | 0.1128 | {'accuracy': 0.9519650655021834} | {'f1': 0.9340521676882466} | {'precision': 0.9297478991596638} | {'recall': 0.9403157489410859} |
| 0.2545 | 12.9956 | 744 | 0.1193 | {'accuracy': 0.9650655021834061} | {'f1': 0.947008806419719} | {'precision': 0.9586734693877551} | {'recall': 0.9386626478828356} |
| 0.1341 | 13.9913 | 801 | 0.1022 | {'accuracy': 0.9650655021834061} | {'f1': 0.941991341991342} | {'precision': 0.9563909774436091} | {'recall': 0.9333846746245668} |
| 0.1833 | 14.9869 | 858 | 0.1094 | {'accuracy': 0.9563318777292577} | {'f1': 0.9386446886446886} | {'precision': 0.9352240896358543} | {'recall': 0.9430111667308434} |
| 0.2267 | 16.0 | 916 | 0.1074 | {'accuracy': 0.9650655021834061} | {'f1': 0.9454619454619454} | {'precision': 0.9512987012987013} | {'recall': 0.9408933384674626} |
| 0.2109 | 16.9956 | 973 | 0.1028 | {'accuracy': 0.9650655021834061} | {'f1': 0.9454619454619454} | {'precision': 0.9512987012987013} | {'recall': 0.9408933384674626} |
| 0.2643 | 17.9913 | 1030 | 0.1110 | {'accuracy': 0.9475982532751092} | {'f1': 0.9370300751879699} | {'precision': 0.9312015503875969} | {'recall': 0.9601463226800154} |
| 0.1624 | 18.9869 | 1087 | 0.0891 | {'accuracy': 0.9781659388646288} | {'f1': 0.9625090929438755} | {'precision': 0.9876847290640394} | {'recall': 0.9489795918367347} |
| 0.144 | 20.0 | 1145 | 0.0990 | {'accuracy': 0.9475982532751092} | {'f1': 0.9296218487394958} | {'precision': 0.9248461289277615} | {'recall': 0.9376203311513285} |
| 0.1473 | 20.9956 | 1202 | 0.0907 | {'accuracy': 0.9650655021834061} | {'f1': 0.941991341991342} | {'precision': 0.9563909774436091} | {'recall': 0.9333846746245668} |
| 0.1364 | 21.9913 | 1259 | 0.0935 | {'accuracy': 0.9519650655021834} | {'f1': 0.9271126934678336} | {'precision': 0.9291819291819292} | {'recall': 0.9252984212552945} |
| 0.184 | 22.9869 | 1316 | 0.0906 | {'accuracy': 0.9563318777292577} | {'f1': 0.9386446886446886} | {'precision': 0.9352240896358543} | {'recall': 0.9430111667308434} |
| 0.149 | 24.0 | 1374 | 0.0950 | {'accuracy': 0.9475982532751092} | {'f1': 0.932436974789916} | {'precision': 0.926595744680851} | {'recall': 0.9451289949942241} |
| 0.213 | 24.9956 | 1431 | 0.0884 | {'accuracy': 0.9563318777292577} | {'f1': 0.9318274318274318} | {'precision': 0.9367965367965368} | {'recall': 0.927993839045052} |
| 0.1058 | 25.9913 | 1488 | 0.0876 | {'accuracy': 0.9694323144104804} | {'f1': 0.9475127301214259} | {'precision': 0.9693486590038314} | {'recall': 0.9360800924143241} |
| 0.1216 | 26.9869 | 1545 | 0.0874 | {'accuracy': 0.9563318777292577} | {'f1': 0.9318274318274318} | {'precision': 0.9367965367965368} | {'recall': 0.927993839045052} |
| 0.1126 | 28.0 | 1603 | 0.0895 | {'accuracy': 0.9650655021834061} | {'f1': 0.941991341991342} | {'precision': 0.9563909774436091} | {'recall': 0.9333846746245668} |
| 0.1325 | 28.9956 | 1660 | 0.0894 | {'accuracy': 0.9563318777292577} | {'f1': 0.9318274318274318} | {'precision': 0.9367965367965368} | {'recall': 0.927993839045052} |
| 0.1197 | 29.8690 | 1710 | 0.0886 | {'accuracy': 0.9563318777292577} | {'f1': 0.9318274318274318} | {'precision': 0.9367965367965368} | {'recall': 0.927993839045052} |
Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1