finetuned-vit-base-patch16-224-upside-down-detector
This model is a fine-tuned version of vit-base-patch16-224-in21k on the custom image orientation dataset adapted from the beans dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.8947
Training and evaluation data
The custom dataset for image orientation adapted from beans dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below:
| Split | # examples |
|---|---|
| train | 2068 |
| validation | 133 |
| test | 128 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32
- num_epochs: 5
Training results
| Epoch | Accuracy |
|---|---|
| 0 | 0.8609 |
| 1 | 0.8835 |
| 2 | 0.8571 |
| 3 | 0.8941 |
| 4 | 0.8941 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Pytorch/XLA 1.9
- Datasets 2.0.0
- Tokenizers 0.12.0
- Downloads last month
- 15
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.