swin-tiny-patch4-window7-224-ve-U11-b-60

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: 0.7230
• Accuracy: 0.8043

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: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	6	1.3859	0.1304
1.3858	2.0	13	1.3818	0.2609
1.3858	2.92	19	1.3723	0.2609
1.3747	4.0	26	1.3355	0.2174
1.3001	4.92	32	1.2625	0.3696
1.3001	6.0	39	1.1306	0.4565
1.141	6.92	45	1.0510	0.4783
0.9784	8.0	52	0.9585	0.5435
0.9784	8.92	58	0.9895	0.4783
0.8533	10.0	65	0.9512	0.5
0.7564	10.92	71	0.9522	0.5217
0.7564	12.0	78	0.9144	0.5
0.6735	12.92	84	0.9070	0.6087
0.5919	14.0	91	0.7915	0.6522
0.5919	14.92	97	0.7989	0.6522
0.504	16.0	104	0.9510	0.6522
0.4422	16.92	110	0.8196	0.6739
0.4422	18.0	117	0.6629	0.7609
0.4031	18.92	123	0.8767	0.6522
0.3752	20.0	130	0.8253	0.6739
0.3752	20.92	136	0.7183	0.7391
0.3424	22.0	143	0.8852	0.6739
0.3424	22.92	149	0.7360	0.7391
0.3293	24.0	156	0.7230	0.8043
0.2822	24.92	162	0.8271	0.6957
0.2822	26.0	169	0.7443	0.8043
0.2623	26.92	175	0.9371	0.6739
0.2807	28.0	182	0.7392	0.7391
0.2807	28.92	188	0.8754	0.6739
0.223	30.0	195	0.7146	0.7826
0.2185	30.92	201	0.7702	0.7391
0.2185	32.0	208	0.7330	0.7174
0.2157	32.92	214	0.8817	0.6957
0.2011	34.0	221	0.7460	0.7174
0.2011	34.92	227	0.9663	0.6739
0.2204	36.0	234	0.8056	0.7174
0.1856	36.92	240	0.7799	0.7174
0.1856	38.0	247	0.8410	0.6957
0.1678	38.92	253	0.7334	0.7391
0.1682	40.0	260	0.8508	0.6957
0.1682	40.92	266	0.8106	0.6957
0.1638	42.0	273	0.8403	0.7174
0.1638	42.92	279	0.9157	0.6957
0.1573	44.0	286	0.9271	0.7391
0.1476	44.92	292	0.9167	0.7174
0.1476	46.0	299	0.9309	0.7174
0.1466	46.92	305	0.8236	0.7826
0.1457	48.0	312	0.8835	0.7826
0.1457	48.92	318	0.9162	0.7391
0.1625	50.0	325	0.8969	0.7391
0.1163	50.92	331	0.9183	0.7391
0.1163	52.0	338	0.9173	0.7391
0.1375	52.92	344	0.8886	0.7609
0.1379	54.0	351	0.8771	0.7391
0.1379	54.92	357	0.8857	0.7391
0.1321	55.38	360	0.8884	0.7391

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0