README.md

metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7654086342655444

Boya1_3Class_SGD_1-e3_20Epoch_Beit-large-224_fold1

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

• Loss: 0.5544
• Accuracy: 0.7654

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: 0.001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• 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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9015	1.0	924	0.9341	0.6112
0.8409	2.0	1848	0.7990	0.6780
0.787	3.0	2772	0.7073	0.7116
0.638	4.0	3696	0.6605	0.7269
0.7348	5.0	4620	0.6355	0.7355
0.5951	6.0	5544	0.6177	0.7396
0.6333	7.0	6468	0.6036	0.7456
0.5506	8.0	7392	0.5938	0.7491
0.5249	9.0	8316	0.5879	0.7526
0.7137	10.0	9240	0.5788	0.7570
0.5106	11.0	10164	0.5775	0.7575
0.6297	12.0	11088	0.5685	0.7608
0.5637	13.0	12012	0.5647	0.7613
0.5879	14.0	12936	0.5617	0.7632
0.4454	15.0	13860	0.5588	0.7649
0.522	16.0	14784	0.5607	0.7630
0.551	17.0	15708	0.5549	0.7660
0.6177	18.0	16632	0.5560	0.7641
0.5656	19.0	17556	0.5549	0.7641
0.6413	20.0	18480	0.5544	0.7654

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1
• Datasets 2.12.0
• Tokenizers 0.13.2