README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: swin-large-patch4-window7-224-fv-finetuned-memes
    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: 0.8601236476043277
          - name: Precision
            type: precision
            value: 0.8582306285016578
          - name: Recall
            type: recall
            value: 0.8601236476043277
          - name: F1
            type: f1
            value: 0.8582797853944862

swin-large-patch4-window7-224-fv-finetuned-memes

This model is a fine-tuned version of microsoft/swin-large-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.6502
• Accuracy: 0.8601
• Precision: 0.8582
• Recall: 0.8601
• F1: 0.8583

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: 0.00012
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• 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	Precision	Recall	F1
1.2077	0.99	20	0.9499	0.6461	0.6764	0.6461	0.5863
0.5687	1.99	40	0.5365	0.7975	0.8018	0.7975	0.7924
0.3607	2.99	60	0.4007	0.8423	0.8419	0.8423	0.8398
0.203	3.99	80	0.3751	0.8509	0.8502	0.8509	0.8503
0.1728	4.99	100	0.4168	0.8509	0.8519	0.8509	0.8506
0.0963	5.99	120	0.4351	0.8586	0.8573	0.8586	0.8555
0.0956	6.99	140	0.4415	0.8547	0.8542	0.8547	0.8541
0.079	7.99	160	0.5312	0.8501	0.8475	0.8501	0.8459
0.0635	8.99	180	0.5376	0.8601	0.8578	0.8601	0.8577
0.0593	9.99	200	0.5060	0.8609	0.8615	0.8609	0.8604
0.0656	10.99	220	0.4997	0.8617	0.8573	0.8617	0.8587
0.0561	11.99	240	0.5430	0.8586	0.8604	0.8586	0.8589
0.0523	12.99	260	0.5354	0.8624	0.8643	0.8624	0.8626
0.0489	13.99	280	0.5539	0.8609	0.8572	0.8609	0.8577
0.0487	14.99	300	0.5785	0.8609	0.8591	0.8609	0.8591
0.0485	15.99	320	0.6186	0.8601	0.8578	0.8601	0.8573
0.0518	16.99	340	0.6342	0.8624	0.8612	0.8624	0.8606
0.0432	17.99	360	0.6302	0.8586	0.8598	0.8586	0.8580
0.0469	18.99	380	0.6323	0.8617	0.8606	0.8617	0.8604
0.0426	19.99	400	0.6502	0.8601	0.8582	0.8601	0.8583

Framework versions

• Transformers 4.24.0.dev0
• Pytorch 1.11.0+cu102
• Datasets 2.6.1.dev0
• Tokenizers 0.13.1