metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - renovation
metrics:
  - accuracy
model-index:
  - name: vit-base-beans-demo-v5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: renovation
          type: renovation
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6950596252129472

vit-base-beans-demo-v5

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the renovation dataset. It achieves the following results on the evaluation set:

Loss: 1.2470
Accuracy: 0.6951

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0616	0.17	100	1.0267	0.5818
0.9594	0.34	200	0.9468	0.6073
1.1785	0.51	300	0.9976	0.5869
0.865	0.68	400	0.9288	0.6388
0.8494	0.85	500	0.8573	0.6516
0.8151	1.02	600	0.8729	0.6397
0.5787	1.19	700	0.9067	0.6448
0.7768	1.36	800	0.8996	0.6533
0.6098	1.53	900	0.8460	0.6695
0.6251	1.7	1000	0.8610	0.6704
0.7863	1.87	1100	0.8668	0.6431
0.2595	2.04	1200	0.8725	0.6840
0.2735	2.21	1300	0.9307	0.6746
0.2429	2.39	1400	1.0958	0.6354
0.3224	2.56	1500	1.0305	0.6687
0.1602	2.73	1600	1.0072	0.6746
0.2042	2.9	1700	1.0971	0.6789
0.0604	3.07	1800	1.0817	0.6917
0.0716	3.24	1900	1.1307	0.6925
0.0822	3.41	2000	1.1827	0.6925
0.0889	3.58	2100	1.2424	0.6934
0.0855	3.75	2200	1.2667	0.6899
0.0682	3.92	2300	1.2470	0.6951

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2