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: 0.8947
Accuracy: 0.7

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	0.9655	0.6714
No log	2.0	4	0.9654	0.6571
No log	3.0	6	0.9651	0.6571
No log	4.0	8	0.9647	0.6571
1.0064	5.0	10	0.9641	0.6571
1.0064	6.0	12	0.9635	0.6571
1.0064	7.0	14	0.9629	0.6571
1.0064	8.0	16	0.9623	0.6571
1.0064	9.0	18	0.9617	0.6571
0.9821	10.0	20	0.9611	0.6571
0.9821	11.0	22	0.9607	0.6571
0.9821	12.0	24	0.9604	0.6714
0.9821	13.0	26	0.9601	0.6714
0.9821	14.0	28	0.9597	0.6714
1.0278	15.0	30	0.9592	0.6714
1.0278	16.0	32	0.9581	0.6714
1.0278	17.0	34	0.9567	0.6714
1.0278	18.0	36	0.9551	0.6714
1.0278	19.0	38	0.9534	0.6714
0.9986	20.0	40	0.9514	0.6571
0.9986	21.0	42	0.9493	0.6571
0.9986	22.0	44	0.9472	0.6429
0.9986	23.0	46	0.9452	0.6429
0.9986	24.0	48	0.9434	0.6429
0.9973	25.0	50	0.9420	0.6429
0.9973	26.0	52	0.9405	0.6429
0.9973	27.0	54	0.9387	0.6286
0.9973	28.0	56	0.9376	0.6286
0.9973	29.0	58	0.9368	0.6429
0.9936	30.0	60	0.9362	0.6429
0.9936	31.0	62	0.9361	0.6571
0.9936	32.0	64	0.9364	0.6714
0.9936	33.0	66	0.9371	0.6714
0.9936	34.0	68	0.9380	0.6429
0.9746	35.0	70	0.9380	0.6571
0.9746	36.0	72	0.9375	0.6714
0.9746	37.0	74	0.9380	0.6714
0.9746	38.0	76	0.9375	0.6714
0.9746	39.0	78	0.9370	0.6714
1.0113	40.0	80	0.9362	0.6714
1.0113	41.0	82	0.9341	0.6714
1.0113	42.0	84	0.9301	0.6857
1.0113	43.0	86	0.9260	0.6714
1.0113	44.0	88	0.9224	0.6571
0.9756	45.0	90	0.9190	0.6714
0.9756	46.0	92	0.9154	0.6714
0.9756	47.0	94	0.9123	0.6714
0.9756	48.0	96	0.9091	0.6571
0.9756	49.0	98	0.9071	0.6571
0.9721	50.0	100	0.9056	0.6571
0.9721	51.0	102	0.9047	0.6571
0.9721	52.0	104	0.9039	0.6571
0.9721	53.0	106	0.9031	0.6714
0.9721	54.0	108	0.9025	0.6714
0.9698	55.0	110	0.9023	0.6714
0.9698	56.0	112	0.9012	0.6714
0.9698	57.0	114	0.8997	0.6714
0.9698	58.0	116	0.8982	0.6714
0.9698	59.0	118	0.8970	0.6714
0.9341	60.0	120	0.8957	0.6857
0.9341	61.0	122	0.8947	0.7
0.9341	62.0	124	0.8940	0.7
0.9341	63.0	126	0.8941	0.6714
0.9341	64.0	128	0.8934	0.6714
0.9717	65.0	130	0.8917	0.6714
0.9717	66.0	132	0.8898	0.6857
0.9717	67.0	134	0.8884	0.6857
0.9717	68.0	136	0.8870	0.6857
0.9717	69.0	138	0.8854	0.6857
0.9655	70.0	140	0.8840	0.6857
0.9655	71.0	142	0.8827	0.6857
0.9655	72.0	144	0.8814	0.6857
0.9655	73.0	146	0.8805	0.6857
0.9655	74.0	148	0.8803	0.6857
0.9458	75.0	150	0.8802	0.6857
0.9458	76.0	152	0.8797	0.6714
0.9458	77.0	154	0.8794	0.6714
0.9458	78.0	156	0.8796	0.6714
0.9458	79.0	158	0.8808	0.6714
0.9094	80.0	160	0.8817	0.6714
0.9094	81.0	162	0.8828	0.6714
0.9094	82.0	164	0.8836	0.6714
0.9094	83.0	166	0.8830	0.6714
0.9094	84.0	168	0.8821	0.6571
0.8719	85.0	170	0.8813	0.6571
0.8719	86.0	172	0.8804	0.6714
0.8719	87.0	174	0.8798	0.6571
0.8719	88.0	176	0.8787	0.6571
0.8719	89.0	178	0.8770	0.6571
0.9288	90.0	180	0.8752	0.6857
0.9288	91.0	182	0.8722	0.6857
0.9288	92.0	184	0.8694	0.6714
0.9288	93.0	186	0.8670	0.6714
0.9288	94.0	188	0.8645	0.6857
0.9039	95.0	190	0.8624	0.6857
0.9039	96.0	192	0.8603	0.6714
0.9039	97.0	194	0.8584	0.6857
0.9039	98.0	196	0.8566	0.6857
0.9039	99.0	198	0.8553	0.6857
0.9081	100.0	200	0.8550	0.6857
0.9081	101.0	202	0.8551	0.6857
0.9081	102.0	204	0.8556	0.6857
0.9081	103.0	206	0.8558	0.6857
0.9081	104.0	208	0.8554	0.6857
0.9142	105.0	210	0.8551	0.6857
0.9142	106.0	212	0.8553	0.6857
0.9142	107.0	214	0.8551	0.6857
0.9142	108.0	216	0.8549	0.6857
0.9142	109.0	218	0.8549	0.6857
0.9347	110.0	220	0.8551	0.6714
0.9347	111.0	222	0.8554	0.6714
0.9347	112.0	224	0.8548	0.6714
0.9347	113.0	226	0.8538	0.6714
0.9347	114.0	228	0.8525	0.6714
0.8922	115.0	230	0.8512	0.6857
0.8922	116.0	232	0.8505	0.6857
0.8922	117.0	234	0.8495	0.6857
0.8922	118.0	236	0.8484	0.6857
0.8922	119.0	238	0.8472	0.6857
0.8897	120.0	240	0.8456	0.6857
0.8897	121.0	242	0.8440	0.6857
0.8897	122.0	244	0.8426	0.6714
0.8897	123.0	246	0.8412	0.6857
0.8897	124.0	248	0.8396	0.6857
0.8829	125.0	250	0.8384	0.6857
0.8829	126.0	252	0.8373	0.6857
0.8829	127.0	254	0.8365	0.6857
0.8829	128.0	256	0.8360	0.6857
0.8829	129.0	258	0.8353	0.6857
0.8744	130.0	260	0.8344	0.6857
0.8744	131.0	262	0.8337	0.6714
0.8744	132.0	264	0.8329	0.6857
0.8744	133.0	266	0.8325	0.6857
0.8744	134.0	268	0.8318	0.6857
0.8657	135.0	270	0.8312	0.6857
0.8657	136.0	272	0.8306	0.6714
0.8657	137.0	274	0.8300	0.6714
0.8657	138.0	276	0.8296	0.6714
0.8657	139.0	278	0.8294	0.6714
0.9421	140.0	280	0.8292	0.6714
0.9421	141.0	282	0.8291	0.6714
0.9421	142.0	284	0.8290	0.6714
0.9421	143.0	286	0.8290	0.6857
0.9421	144.0	288	0.8289	0.6857
0.9066	145.0	290	0.8287	0.6857
0.9066	146.0	292	0.8290	0.6857
0.9066	147.0	294	0.8293	0.6857
0.9066	148.0	296	0.8294	0.6857
0.9066	149.0	298	0.8295	0.6857
0.9068	150.0	300	0.8295	0.6857
0.9068	151.0	302	0.8294	0.6857
0.9068	152.0	304	0.8293	0.6857
0.9068	153.0	306	0.8293	0.6857
0.9068	154.0	308	0.8290	0.6857
0.8715	155.0	310	0.8287	0.6857
0.8715	156.0	312	0.8283	0.6857
0.8715	157.0	314	0.8277	0.6857
0.8715	158.0	316	0.8274	0.6857
0.8715	159.0	318	0.8269	0.6857
0.8921	160.0	320	0.8266	0.6857
0.8921	161.0	322	0.8264	0.6857
0.8921	162.0	324	0.8261	0.6857
0.8921	163.0	326	0.8260	0.6857
0.8921	164.0	328	0.8258	0.6857
0.8768	165.0	330	0.8252	0.6857
0.8768	166.0	332	0.8248	0.6857
0.8768	167.0	334	0.8243	0.6857
0.8768	168.0	336	0.8237	0.6857
0.8768	169.0	338	0.8231	0.6857
0.8519	170.0	340	0.8227	0.6857
0.8519	171.0	342	0.8223	0.6857
0.8519	172.0	344	0.8221	0.6857
0.8519	173.0	346	0.8220	0.6857
0.8519	174.0	348	0.8218	0.6857
0.92	175.0	350	0.8215	0.6857
0.92	176.0	352	0.8211	0.7
0.92	177.0	354	0.8207	0.7
0.92	178.0	356	0.8204	0.7
0.92	179.0	358	0.8200	0.7
0.879	180.0	360	0.8197	0.7
0.879	181.0	362	0.8194	0.7
0.879	182.0	364	0.8191	0.6857
0.879	183.0	366	0.8187	0.6857
0.879	184.0	368	0.8185	0.7
0.8893	185.0	370	0.8182	0.7
0.8893	186.0	372	0.8180	0.7
0.8893	187.0	374	0.8177	0.7
0.8893	188.0	376	0.8176	0.7
0.8893	189.0	378	0.8175	0.7
0.8501	190.0	380	0.8173	0.7
0.8501	191.0	382	0.8171	0.7
0.8501	192.0	384	0.8170	0.7
0.8501	193.0	386	0.8169	0.7
0.8501	194.0	388	0.8169	0.7
0.8611	195.0	390	0.8168	0.7
0.8611	196.0	392	0.8168	0.7
0.8611	197.0	394	0.8168	0.7
0.8611	198.0	396	0.8168	0.7
0.8611	199.0	398	0.8168	0.7
0.8881	200.0	400	0.8168	0.7

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Niraya666/swin-tiny-patch4-window7-224-finetuned-ADC-4cls-0922

swin-tiny-patch4-window7-224-finetuned-ADC-4cls-0922

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Niraya666/
swin-tiny-patch4-window7-224-finetuned-ADC-4cls-0922