metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-PE
    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: 0.797979797979798

swin-tiny-patch4-window7-224-PE

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: 0.4489
Accuracy: 0.7980

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: 0.0025
train_batch_size: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6872	1.0	11	0.6535	0.6061
0.7287	2.0	22	0.6601	0.6397
0.7212	3.0	33	0.6740	0.5657
0.6947	4.0	44	0.6531	0.6532
0.6783	5.0	55	0.6739	0.5724
0.6816	6.0	66	0.6274	0.6599
0.6428	7.0	77	0.6671	0.6330
0.6928	8.0	88	0.6380	0.6498
0.6767	9.0	99	0.6875	0.6061
0.6918	10.0	110	0.6859	0.5690
0.6845	11.0	121	0.6810	0.5657
0.6826	12.0	132	0.6919	0.5185
0.6877	13.0	143	0.6693	0.6061
0.6709	14.0	154	0.6660	0.5690
0.6707	15.0	165	0.6764	0.5690
0.6703	16.0	176	0.6467	0.6296
0.6629	17.0	187	0.6471	0.6431
0.6557	18.0	198	0.6597	0.6229
0.659	19.0	209	0.6451	0.6027
0.65	20.0	220	0.6638	0.6094
0.6453	21.0	231	0.6544	0.6162
0.6426	22.0	242	0.6565	0.5825
0.6339	23.0	253	0.6743	0.6296
0.6236	24.0	264	0.6669	0.5960
0.6427	25.0	275	0.6379	0.6532
0.6439	26.0	286	0.6361	0.6263
0.6212	27.0	297	0.6540	0.6465
0.6186	28.0	308	0.5925	0.6700
0.6162	29.0	319	0.6224	0.6734
0.6237	30.0	330	0.6018	0.6667
0.6061	31.0	341	0.5735	0.6801
0.6138	32.0	352	0.6425	0.6566
0.595	33.0	363	0.5827	0.6768
0.5869	34.0	374	0.5956	0.7172
0.577	35.0	385	0.5458	0.7003
0.5766	36.0	396	0.5603	0.6869
0.5726	37.0	407	0.5339	0.7340
0.5702	38.0	418	0.5577	0.7138
0.5762	39.0	429	0.5262	0.7374
0.5543	40.0	440	0.5091	0.7441
0.5339	41.0	451	0.5185	0.7542
0.5428	42.0	462	0.5023	0.7542
0.5349	43.0	473	0.5439	0.7306
0.5319	44.0	484	0.4745	0.7811
0.5294	45.0	495	0.5432	0.7172
0.5314	46.0	506	0.4511	0.7912
0.5073	47.0	517	0.4379	0.8047
0.5028	48.0	528	0.4487	0.7980
0.4985	49.0	539	0.4550	0.7946
0.4826	50.0	550	0.4489	0.7980

Framework versions

Transformers 4.33.3
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3