README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: swin-base-patch4-window7-224-in22k-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: 0.8562596599690881
          - name: Precision
            type: precision
            value: 0.8545652818321074
          - name: Recall
            type: recall
            value: 0.8562596599690881
          - name: F1
            type: f1
            value: 0.8552274649509984

swin-base-patch4-window7-224-in22k-finetuned-memes

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.7094
• Accuracy: 0.8563
• Precision: 0.8546
• Recall: 0.8563
• F1: 0.8552

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
1.1655	0.99	20	0.8573	0.6955	0.6953	0.6955	0.6683
0.5506	1.99	40	0.5327	0.8083	0.8050	0.8083	0.7963
0.3573	2.99	60	0.4497	0.8338	0.8339	0.8338	0.8317
0.2083	3.99	80	0.4561	0.8354	0.8450	0.8354	0.8368
0.1545	4.99	100	0.4605	0.8423	0.8458	0.8423	0.8430
0.1014	5.99	120	0.4924	0.8524	0.8571	0.8524	0.8538
0.0854	6.99	140	0.5759	0.8393	0.8452	0.8393	0.8400
0.1012	7.99	160	0.5142	0.8362	0.8378	0.8362	0.8361
0.077	8.99	180	0.5647	0.8331	0.8538	0.8331	0.8407
0.0667	9.99	200	0.5294	0.8462	0.8509	0.8462	0.8483
0.0666	10.99	220	0.6038	0.8385	0.8415	0.8385	0.8396
0.0574	11.99	240	0.6384	0.8408	0.8431	0.8408	0.8411
0.0488	12.99	260	0.6305	0.8516	0.8561	0.8516	0.8532
0.0524	13.99	280	0.6411	0.8509	0.8526	0.8509	0.8510
0.0511	14.99	300	0.6462	0.8547	0.8542	0.8547	0.8543
0.0495	15.99	320	0.6869	0.8532	0.8534	0.8532	0.8527
0.0412	16.99	340	0.6643	0.8578	0.8554	0.8578	0.8564
0.0411	17.99	360	0.7214	0.8570	0.8539	0.8570	0.8552
0.0434	18.99	380	0.7037	0.8524	0.8507	0.8524	0.8514
0.0394	19.99	400	0.7094	0.8563	0.8546	0.8563	0.8552

Framework versions

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