metadata

tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-pretraining-2024_04_02-atelectasis-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: 0.7644320297951583

vit-pretraining-2024_04_02-atelectasis-classifier

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

Loss: 0.5020
Accuracy: 0.7644

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
0.6304	1.0	537	0.6342	0.6709
0.5931	2.0	1074	0.5669	0.7207
0.5027	3.0	1611	0.5397	0.7393
0.5659	4.0	2148	0.5341	0.7458
0.5115	5.0	2685	0.5433	0.7346
0.5108	6.0	3222	0.5454	0.7309
0.5187	7.0	3759	0.5136	0.7621
0.4435	8.0	4296	0.5057	0.7677
0.583	9.0	4833	0.5042	0.7584
0.5256	10.0	5370	0.5249	0.7495
0.4818	11.0	5907	0.5212	0.7481
0.5575	12.0	6444	0.5061	0.7481
0.3572	13.0	6981	0.5042	0.7602
0.489	14.0	7518	0.5004	0.7709
0.4773	15.0	8055	0.5074	0.7700
0.4577	16.0	8592	0.5054	0.7677
0.4619	17.0	9129	0.5021	0.7686
0.3865	18.0	9666	0.5074	0.7644
0.4889	19.0	10203	0.5113	0.7598
0.4637	20.0	10740	0.5020	0.7644

Framework versions

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