Chess / README.md
Tapashh's picture
End of training
1a461f2 verified
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
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:258]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6538461538461539

Chess

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: 0.7292
  • Accuracy: 0.6538

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.92 3 1.0620 0.5385
No log 1.85 6 0.9886 0.5962
No log 2.77 9 0.9286 0.7115
0.9947 4.0 13 0.8659 0.6731
0.9947 4.92 16 0.8310 0.6731
0.9947 5.85 19 0.7778 0.6731
0.7638 6.77 22 0.7388 0.7115
0.7638 8.0 26 0.7570 0.6731
0.7638 8.92 29 0.7214 0.6923
0.6277 9.23 30 0.7292 0.6538

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2