metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: beit-base
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cats_vs_dogs
type: cats_vs_dogs
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9976505766766339
beit-base
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the cats_vs_dogs dataset. It achieves the following results on the evaluation set:
- Loss: 0.0116
- Accuracy: 0.9977
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0303 | 1.0 | 585 | 0.0186 | 0.9942 |
| 0.0374 | 2.0 | 1170 | 0.0150 | 0.9955 |
| 0.0559 | 3.0 | 1755 | 0.0116 | 0.9977 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2