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-ve-b-U10-24
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8431372549019608

vit-base-patch16-224-ve-b-U10-24

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: 0.6432
Accuracy: 0.8431

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: 5.5e-05
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: 0.05
num_epochs: 24

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.96	6	1.3827	0.3137
1.378	1.92	12	1.3335	0.5490
1.378	2.88	18	1.2577	0.5882
1.2725	4.0	25	1.1886	0.4706
1.1073	4.96	31	1.1040	0.6275
1.1073	5.92	37	1.0658	0.6078
0.9657	6.88	43	1.0155	0.6667
0.8361	8.0	50	0.9330	0.7451
0.8361	8.96	56	0.9690	0.6667
0.7181	9.92	62	0.8910	0.7255
0.7181	10.88	68	0.8953	0.6863
0.6126	12.0	75	0.8343	0.7451
0.5096	12.96	81	0.8048	0.7059
0.5096	13.92	87	0.7977	0.7059
0.4348	14.88	93	0.7250	0.7451
0.4011	16.0	100	0.6432	0.8431
0.4011	16.96	106	0.7317	0.7255
0.3292	17.92	112	0.7015	0.7451
0.3292	18.88	118	0.6248	0.7647
0.309	20.0	125	0.6990	0.7451
0.2744	20.96	131	0.6591	0.7843
0.2744	21.92	137	0.6452	0.7647
0.2864	22.88	143	0.6290	0.7843
0.2864	23.04	144	0.6285	0.7843

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0