metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-base-patch4-window7-224-20epochs-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: 0.847758887171561
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: custom
          name: custom
          split: test
        metrics:
          - type: f1
            value: 0.8504084378729573
            name: F1
          - type: precision
            value: 0.8519647060733512
            name: Precision
          - type: recall
            value: 0.8523956723338485
            name: Recall

swin-base-patch4-window7-224-20epochs-finetuned-memes

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

Loss: 0.7090
Accuracy: 0.8478

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: 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: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0238	0.99	40	0.9636	0.6445
0.777	1.99	80	0.6591	0.7666
0.4763	2.99	120	0.5381	0.8130
0.3215	3.99	160	0.5244	0.8253
0.2179	4.99	200	0.5123	0.8238
0.1868	5.99	240	0.5052	0.8308
0.154	6.99	280	0.5444	0.8338
0.1166	7.99	320	0.6318	0.8238
0.1099	8.99	360	0.5656	0.8338
0.0925	9.99	400	0.6057	0.8338
0.0779	10.99	440	0.5942	0.8393
0.0629	11.99	480	0.6112	0.8400
0.0742	12.99	520	0.6588	0.8331
0.0752	13.99	560	0.6143	0.8408
0.0577	14.99	600	0.6450	0.8516
0.0589	15.99	640	0.6787	0.8400
0.0555	16.99	680	0.6641	0.8454
0.052	17.99	720	0.7213	0.8524
0.0589	18.99	760	0.6917	0.8470
0.0506	19.99	800	0.7090	0.8478

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1