metadata

tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-pretraining-2024_03_25-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: 0.7648975791433892

vit-pretraining-2024_03_25-classifier

This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.5083
Accuracy: 0.7649

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6422	1.0	537	0.6409	0.6560
0.5509	2.0	1074	0.5966	0.6862
0.5123	3.0	1611	0.5743	0.7044
0.5237	4.0	2148	0.5523	0.7188
0.5589	5.0	2685	0.5352	0.7370
0.5671	6.0	3222	0.5317	0.7407
0.5247	7.0	3759	0.5228	0.7486
0.4855	8.0	4296	0.5422	0.7374
0.5122	9.0	4833	0.5195	0.7477
0.5381	10.0	5370	0.5277	0.7398
0.5465	11.0	5907	0.5213	0.7514
0.4552	12.0	6444	0.5300	0.7495
0.5188	13.0	6981	0.5107	0.7505
0.5056	14.0	7518	0.5075	0.7579
0.4759	15.0	8055	0.5077	0.7644
0.6042	16.0	8592	0.5143	0.7602
0.4002	17.0	9129	0.5184	0.7612
0.4664	18.0	9666	0.5072	0.7630
0.4653	19.0	10203	0.5103	0.7626
0.4096	20.0	10740	0.5083	0.7649

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2