metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-AHB-against-NotAHB
    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: 0.9538461538461539

vit-base-patch16-224-AHB-against-NotAHB

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1588
Accuracy: 0.9538

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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8421	4	0.7464	0.6462
No log	1.8947	9	0.4871	0.8154
0.7361	2.9474	14	0.6255	0.7385
0.7361	4.0	19	0.3812	0.8308
0.68	4.8421	23	0.3080	0.8615
0.68	5.8947	28	0.2427	0.9231
0.4598	6.9474	33	0.2114	0.9077
0.4598	8.0	38	0.2383	0.9231
0.4265	8.8421	42	0.3264	0.8462
0.4265	9.8947	47	0.2211	0.8769
0.3265	10.9474	52	0.2056	0.9077
0.3265	12.0	57	0.4595	0.7538
0.3282	12.8421	61	0.1888	0.9385
0.3282	13.8947	66	0.1588	0.9538
0.296	14.9474	71	0.3073	0.8769
0.296	16.0	76	0.1888	0.9231
0.2679	16.8421	80	0.2230	0.9077

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1