swin-tiny-patch4-window7-224-finetuned-eurosat

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: 0.3266
• Accuracy: 0.8465

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2941	1.0	17	1.1717	0.4689
1.0655	2.0	34	0.9397	0.5560
0.8008	3.0	51	0.6153	0.7303
0.7204	4.0	68	0.5665	0.7427
0.6931	5.0	85	0.4670	0.7801
0.6277	6.0	102	0.4328	0.8465
0.5689	7.0	119	0.4078	0.8174
0.6103	8.0	136	0.4060	0.8091
0.5501	9.0	153	0.4842	0.7884
0.6018	10.0	170	0.3780	0.8423
0.5668	11.0	187	0.3551	0.8631
0.5192	12.0	204	0.4514	0.8216
0.5133	13.0	221	0.3598	0.8174
0.5753	14.0	238	0.4172	0.8091
0.4833	15.0	255	0.4685	0.8050
0.5546	16.0	272	0.4474	0.7842
0.5179	17.0	289	0.4570	0.7884
0.5017	18.0	306	0.4218	0.8050
0.4808	19.0	323	0.4094	0.8050
0.4708	20.0	340	0.4693	0.7759
0.5033	21.0	357	0.3141	0.8672
0.4859	22.0	374	0.3687	0.8257
0.516	23.0	391	0.3819	0.8216
0.4822	24.0	408	0.3391	0.8506
0.4748	25.0	425	0.3281	0.8506
0.4914	26.0	442	0.3308	0.8631
0.4354	27.0	459	0.3859	0.8133
0.4297	28.0	476	0.3761	0.8133
0.4747	29.0	493	0.2914	0.8672
0.4395	30.0	510	0.3025	0.8548
0.4279	31.0	527	0.3314	0.8506
0.4327	32.0	544	0.4626	0.7842
0.446	33.0	561	0.3499	0.8382
0.4011	34.0	578	0.3408	0.8465
0.4418	35.0	595	0.3159	0.8589
0.484	36.0	612	0.3130	0.8548
0.4119	37.0	629	0.2899	0.8589
0.4453	38.0	646	0.3200	0.8465
0.4074	39.0	663	0.3493	0.8465
0.3937	40.0	680	0.3003	0.8672
0.4222	41.0	697	0.3547	0.8299
0.3922	42.0	714	0.3206	0.8589
0.3973	43.0	731	0.4074	0.8133
0.4118	44.0	748	0.3147	0.8589
0.4088	45.0	765	0.3393	0.8506
0.3635	46.0	782	0.3584	0.8257
0.403	47.0	799	0.3240	0.8506
0.3943	48.0	816	0.3536	0.8216
0.4085	49.0	833	0.3270	0.8465
0.3865	50.0	850	0.3266	0.8465

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1