msi-resnet18
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Accuracy: 0.7618
- F1: 0.7034
- Precision: 0.7032
- Recall: 0.7035
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: 1e-06
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|
| 0.6225 | 1.0 | 1970 | 0.6131 | 0.6718 | 0.5197 | 0.6298 | 0.4424 |
| 0.5749 | 2.0 | 3941 | 0.5577 | 0.7138 | 0.6061 | 0.6771 | 0.5486 |
| 0.5506 | 3.0 | 5911 | 0.5347 | 0.7355 | 0.6367 | 0.7096 | 0.5773 |
| 0.5304 | 4.0 | 7882 | 0.5114 | 0.7501 | 0.6615 | 0.7250 | 0.6082 |
| 0.5196 | 5.0 | 9852 | 0.5057 | 0.7503 | 0.6932 | 0.6838 | 0.7028 |
| 0.5125 | 6.0 | 11823 | 0.4920 | 0.7610 | 0.6983 | 0.7078 | 0.6890 |
| 0.5016 | 7.0 | 13793 | 0.4929 | 0.7578 | 0.7055 | 0.6890 | 0.7228 |
| 0.4871 | 8.0 | 15764 | 0.4796 | 0.7683 | 0.7042 | 0.7222 | 0.6870 |
| 0.5069 | 9.0 | 17734 | 0.4766 | 0.7743 | 0.6996 | 0.7512 | 0.6545 |
| 0.5059 | 10.0 | 19700 | 0.4869 | 0.7618 | 0.7034 | 0.7032 | 0.7035 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
