<!-- 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-DogSick
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3041
- Acc: {'accuracy': 0.6102564102564103}
- F1: {'f1': 0.5980148081337936}
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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | F1 |
|---|---|---|---|---|---|
| 2.4055 | 0.61 | 50 | 2.2086 | {'accuracy': 0.41794871794871796} | {'f1': 0.3246788612052483} |
| 2.0379 | 1.22 | 100 | 1.9233 | {'accuracy': 0.4846153846153846} | {'f1': 0.4386383497855148} |
| 1.7287 | 1.83 | 150 | 1.7200 | {'accuracy': 0.5256410256410257} | {'f1': 0.4806042289317683} |
| 1.4667 | 2.44 | 200 | 1.6021 | {'accuracy': 0.5692307692307692} | {'f1': 0.533374137436958} |
| 1.3444 | 3.05 | 250 | 1.5410 | {'accuracy': 0.5333333333333333} | {'f1': 0.4846134797922835} |
| 1.1334 | 3.66 | 300 | 1.4674 | {'accuracy': 0.5743589743589743} | {'f1': 0.5533432367508125} |
| 1.007 | 4.27 | 350 | 1.4096 | {'accuracy': 0.5923076923076923} | {'f1': 0.5801847507206119} |
| 0.897 | 4.88 | 400 | 1.3674 | {'accuracy': 0.6} | {'f1': 0.5903283954748092} |
| 0.7326 | 5.49 | 450 | 1.3359 | {'accuracy': 0.5923076923076923} | {'f1': 0.5793036546532927} |
| 0.7105 | 6.1 | 500 | 1.3259 | {'accuracy': 0.6153846153846154} | {'f1': 0.6064330281486513} |
| 0.6164 | 6.71 | 550 | 1.3183 | {'accuracy': 0.6102564102564103} | {'f1': 0.6014695572651212} |
| 0.5804 | 7.32 | 600 | 1.3103 | {'accuracy': 0.6025641025641025} | {'f1': 0.5965366941171513} |
| 0.5313 | 7.93 | 650 | 1.3041 | {'accuracy': 0.6102564102564103} | {'f1': 0.5980148081337936} |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
