metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Chess_images_classifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9
Chess_images_classifier
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: 1.0591
- Accuracy: 0.9
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: 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 1.7966 | 0.1 |
| No log | 2.0 | 4 | 1.7835 | 0.2 |
| No log | 3.0 | 6 | 1.7547 | 0.2667 |
| No log | 4.0 | 8 | 1.7069 | 0.3667 |
| 1.7198 | 5.0 | 10 | 1.6416 | 0.3667 |
| 1.7198 | 6.0 | 12 | 1.5306 | 0.4 |
| 1.7198 | 7.0 | 14 | 1.4958 | 0.5333 |
| 1.7198 | 8.0 | 16 | 1.4440 | 0.5333 |
| 1.7198 | 9.0 | 18 | 1.3930 | 0.6 |
| 1.3635 | 10.0 | 20 | 1.2984 | 0.7333 |
| 1.3635 | 11.0 | 22 | 1.3484 | 0.7333 |
| 1.3635 | 12.0 | 24 | 1.2727 | 0.8333 |
| 1.3635 | 13.0 | 26 | 1.1674 | 0.8333 |
| 1.3635 | 14.0 | 28 | 1.1443 | 0.8667 |
| 1.0916 | 15.0 | 30 | 1.1607 | 0.9 |
| 1.0916 | 16.0 | 32 | 1.1076 | 0.8667 |
| 1.0916 | 17.0 | 34 | 1.0670 | 0.9667 |
| 1.0916 | 18.0 | 36 | 1.0694 | 0.9333 |
| 1.0916 | 19.0 | 38 | 1.0874 | 0.9 |
| 0.9397 | 20.0 | 40 | 1.0591 | 0.9 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2