metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-agrivision
    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: 0.9362186788154897

swin-tiny-patch4-window7-224-finetuned-agrivision

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: 0.2783
Accuracy: 0.9362

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: 5e-05
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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5829	1.0	31	0.7480	0.7267
0.1199	2.0	62	0.4407	0.8246
0.1028	3.0	93	0.4477	0.8246
0.0533	4.0	124	0.4606	0.8292
0.0411	5.0	155	0.2470	0.9180
0.022	6.0	186	0.1568	0.9544
0.0206	7.0	217	0.4187	0.8793
0.0069	8.0	248	0.2498	0.9203
0.0053	9.0	279	0.2654	0.9226
0.0094	10.0	310	0.2343	0.9385
0.0152	11.0	341	0.3421	0.9021
0.0047	12.0	372	0.4494	0.8724
0.0128	13.0	403	0.5360	0.8679
0.0024	14.0	434	0.2775	0.9112
0.0127	15.0	465	0.2911	0.8975
0.0038	16.0	496	0.2337	0.9294
0.0001	17.0	527	0.2207	0.9408
0.0054	18.0	558	0.2506	0.9362
0.0011	19.0	589	0.3778	0.8952
0.0002	20.0	620	0.2316	0.9408
0.0003	21.0	651	0.2133	0.9431
0.0009	22.0	682	0.2519	0.9339
0.0004	23.0	713	0.2931	0.9203
0.0001	24.0	744	0.2847	0.9271
0.0003	25.0	775	0.2831	0.9317
0.0008	26.0	806	0.2919	0.9271
0.0003	27.0	837	0.2798	0.9362
0.0008	28.0	868	0.2857	0.9362
0.0008	29.0	899	0.2780	0.9362
0.0013	30.0	930	0.2783	0.9362

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1