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: 0.9202733485193622

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: 0.3605
Accuracy: 0.9203

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
0.5913	1.0	31	0.7046	0.7175
0.1409	2.0	62	0.8423	0.6788
0.0825	3.0	93	0.6224	0.7654
0.0509	4.0	124	0.4379	0.8360
0.0439	5.0	155	0.1706	0.9317
0.0107	6.0	186	0.1914	0.9362
0.0134	7.0	217	0.2491	0.9089
0.0338	8.0	248	0.2119	0.9362
0.0306	9.0	279	0.4502	0.8610
0.0054	10.0	310	0.4990	0.8747
0.0033	11.0	341	0.2746	0.9112
0.0021	12.0	372	0.2501	0.9317
0.0068	13.0	403	0.1883	0.9522
0.0038	14.0	434	0.3672	0.9134
0.0006	15.0	465	0.2275	0.9408
0.0011	16.0	496	0.3349	0.9134
0.0017	17.0	527	0.3329	0.9157
0.0007	18.0	558	0.2508	0.9317
0.0023	19.0	589	0.2338	0.9385
0.0003	20.0	620	0.3193	0.9226
0.002	21.0	651	0.4604	0.9043
0.0023	22.0	682	0.3338	0.9203
0.005	23.0	713	0.2925	0.9271
0.0001	24.0	744	0.2022	0.9522
0.0002	25.0	775	0.2699	0.9339
0.0007	26.0	806	0.2603	0.9385
0.0005	27.0	837	0.4120	0.9134
0.0003	28.0	868	0.3550	0.9203
0.0008	29.0	899	0.3657	0.9203
0.0	30.0	930	0.3605	0.9203

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1