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: 0.2644
Accuracy: 0.9779

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7081	0.9935	38	1.6642	0.2868
1.4025	1.9869	76	1.3761	0.4632
1.0918	2.9804	114	1.0276	0.5515
0.8051	4.0	153	0.7679	0.6691
0.635	4.9935	191	0.5928	0.7868
0.6051	5.9869	229	0.6957	0.75
0.5539	6.9804	267	0.5016	0.7941
0.4683	8.0	306	0.4733	0.8235
0.4153	8.9935	344	0.4835	0.8529
0.3954	9.9869	382	0.5431	0.8309
0.3524	10.9804	420	0.4061	0.8235
0.3546	12.0	459	0.4925	0.8382
0.2922	12.9935	497	0.3637	0.875
0.2342	13.9869	535	0.3286	0.8971
0.2083	14.9804	573	0.3271	0.8824
0.2704	16.0	612	0.3700	0.8824
0.1871	16.9935	650	0.3447	0.8971
0.226	17.9869	688	0.4280	0.8603
0.245	18.9804	726	0.6445	0.8088
0.1545	20.0	765	0.4180	0.8603
0.0981	20.9935	803	0.3208	0.9044
0.1455	21.9869	841	0.4256	0.8603
0.2405	22.9804	879	0.3474	0.8971
0.1549	24.0	918	0.3940	0.9044
0.1721	24.9935	956	0.4279	0.8824
0.1378	25.9869	994	0.3871	0.9044
0.0924	26.9804	1032	0.7301	0.8456
0.1325	28.0	1071	0.3712	0.9044
0.1426	28.9935	1109	0.4400	0.8603
0.0866	29.9869	1147	0.2779	0.9412
0.0659	30.9804	1185	0.3207	0.9412
0.1175	32.0	1224	0.4339	0.9044
0.0455	32.9935	1262	0.4537	0.9265
0.1006	33.9869	1300	0.6521	0.875
0.033	34.9804	1338	0.5616	0.9044
0.0979	36.0	1377	0.3718	0.9191
0.1045	36.9935	1415	0.2529	0.9632
0.0815	37.9869	1453	0.3511	0.9338
0.0761	38.9804	1491	0.3114	0.9338
0.0747	40.0	1530	0.2837	0.9338
0.0545	40.9935	1568	0.4269	0.9412
0.0796	41.9869	1606	0.2331	0.9412
0.055	42.9804	1644	0.2900	0.9485
0.0706	44.0	1683	0.3368	0.9632
0.0505	44.9935	1721	0.3780	0.9485
0.0698	45.9869	1759	0.4822	0.9191
0.0275	46.9804	1797	0.3434	0.9632
0.0641	48.0	1836	0.3387	0.9706
0.0484	48.9935	1874	0.5350	0.9191
0.0388	49.9869	1912	0.3826	0.9118
0.0347	50.9804	1950	0.3739	0.9559
0.1046	52.0	1989	0.3075	0.9118
0.0298	52.9935	2027	0.3558	0.9559
0.0478	53.9869	2065	0.3056	0.9706
0.0285	54.9804	2103	0.2851	0.9632
0.0407	56.0	2142	0.3223	0.9559
0.0459	56.9935	2180	0.4575	0.9485
0.0409	57.9869	2218	0.2930	0.9632
0.0743	58.9804	2256	0.4032	0.9485
0.0346	60.0	2295	0.3738	0.9412
0.0302	60.9935	2333	0.3597	0.9485
0.0488	61.9869	2371	0.2595	0.9559
0.0562	62.9804	2409	0.3764	0.9412
0.0216	64.0	2448	0.2644	0.9779
0.0219	64.9935	2486	0.3092	0.9632
0.0272	65.9869	2524	0.2898	0.9632
0.027	66.9804	2562	0.2693	0.9632
0.0397	68.0	2601	0.3843	0.9412
0.0154	68.9935	2639	0.3051	0.9485
0.0004	69.9869	2677	0.3909	0.9412
0.0651	70.9804	2715	0.2977	0.9485
0.016	72.0	2754	0.2695	0.9632
0.0351	72.9935	2792	0.2720	0.9706
0.0206	73.9869	2830	0.2549	0.9706
0.0109	74.9804	2868	0.2412	0.9706
0.0012	76.0	2907	0.3494	0.9779
0.0418	76.9935	2945	0.3729	0.9632
0.0165	77.9869	2983	0.3471	0.9632
0.0163	78.9804	3021	0.2973	0.9706
0.0202	80.0	3060	0.3730	0.9559
0.0368	80.9935	3098	0.2877	0.9706
0.0374	81.9869	3136	0.4143	0.9632
0.0296	82.9804	3174	0.2895	0.9779
0.0405	84.0	3213	0.2927	0.9559
0.0097	84.9935	3251	0.3179	0.9632
0.0182	85.9869	3289	0.3047	0.9706
0.0207	86.9804	3327	0.3018	0.9779
0.0207	88.0	3366	0.3321	0.9632
0.003	88.9935	3404	0.3086	0.9706
0.0157	89.9869	3442	0.2948	0.9706
0.0428	90.9804	3480	0.3175	0.9706
0.0189	92.0	3519	0.3240	0.9632
0.0046	92.9935	3557	0.3414	0.9632
0.0057	93.9869	3595	0.3329	0.9632
0.0165	94.9804	3633	0.3240	0.9632
0.006	96.0	3672	0.3180	0.9706
0.0172	96.9935	3710	0.3103	0.9779
0.0109	97.9869	3748	0.3035	0.9779
0.0172	98.9804	3786	0.3034	0.9779
0.0219	99.3464	3800	0.3036	0.9779

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

mujerry/swin-tiny-patch4-window7-224-finetuned-papsmear

swin-tiny-patch4-window7-224-finetuned-papsmear

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mujerry/
swin-tiny-patch4-window7-224-finetuned-papsmear