vit-msn-small-beta-fia-manually-enhanced_test_2

This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.5203
Accuracy: 0.7746

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: 1e-05
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: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.5714	1	0.6037	0.7465
No log	1.7143	3	0.6071	0.7324
No log	2.8571	5	0.6120	0.7183
No log	4.0	7	0.6188	0.7183
No log	4.5714	8	0.6206	0.7183
0.4866	5.7143	10	0.6272	0.6972
0.4866	6.8571	12	0.6355	0.6901
0.4866	8.0	14	0.6399	0.6901
0.4866	8.5714	15	0.6364	0.6831
0.4866	9.7143	17	0.6295	0.6831
0.4866	10.8571	19	0.6288	0.6901
0.4519	12.0	21	0.6185	0.6901
0.4519	12.5714	22	0.6159	0.6901
0.4519	13.7143	24	0.6113	0.6972
0.4519	14.8571	26	0.5987	0.6901
0.4519	16.0	28	0.6017	0.6972
0.4519	16.5714	29	0.6067	0.6972
0.437	17.7143	31	0.6062	0.6620
0.437	18.8571	33	0.5966	0.6901
0.437	20.0	35	0.5858	0.7113
0.437	20.5714	36	0.5889	0.7042
0.437	21.7143	38	0.5768	0.7183
0.4353	22.8571	40	0.5752	0.7183
0.4353	24.0	42	0.5729	0.7183
0.4353	24.5714	43	0.5909	0.6972
0.4353	25.7143	45	0.6038	0.6761
0.4353	26.8571	47	0.5904	0.6901
0.4353	28.0	49	0.5847	0.6831
0.4141	28.5714	50	0.5615	0.7113
0.4141	29.7143	52	0.5544	0.7254
0.4141	30.8571	54	0.5904	0.6690
0.4141	32.0	56	0.5948	0.6831
0.4141	32.5714	57	0.5800	0.6972
0.4141	33.7143	59	0.5902	0.6972
0.4066	34.8571	61	0.5950	0.6690
0.4066	36.0	63	0.5500	0.7324
0.4066	36.5714	64	0.5470	0.7324
0.4066	37.7143	66	0.5859	0.6901
0.4066	38.8571	68	0.5955	0.6831
0.3827	40.0	70	0.5967	0.6761
0.3827	40.5714	71	0.5809	0.6901
0.3827	41.7143	73	0.5721	0.6972
0.3827	42.8571	75	0.6019	0.6831
0.3827	44.0	77	0.6071	0.6901
0.3827	44.5714	78	0.5962	0.6972
0.37	45.7143	80	0.6114	0.6831
0.37	46.8571	82	0.5594	0.7183
0.37	48.0	84	0.5493	0.7324
0.37	48.5714	85	0.5744	0.7113
0.37	49.7143	87	0.5443	0.7183
0.37	50.8571	89	0.5469	0.7324
0.3797	52.0	91	0.6003	0.6831
0.3797	52.5714	92	0.6048	0.6901
0.3797	53.7143	94	0.5203	0.7746
0.3797	54.8571	96	0.5327	0.7535
0.3797	56.0	98	0.6414	0.6338
0.3797	56.5714	99	0.6562	0.6197
0.3715	57.7143	101	0.5754	0.7183
0.3715	58.8571	103	0.5672	0.7254
0.3715	60.0	105	0.6060	0.6901
0.3715	60.5714	106	0.6536	0.6197
0.3715	61.7143	108	0.6177	0.6479
0.3483	62.8571	110	0.5385	0.7535
0.3483	64.0	112	0.5630	0.7394
0.3483	64.5714	113	0.5818	0.7254
0.3483	65.7143	115	0.6055	0.6972
0.3483	66.8571	117	0.5737	0.7324
0.3483	68.0	119	0.5606	0.7394
0.3667	68.5714	120	0.5829	0.7183
0.3667	69.7143	122	0.5931	0.7113
0.3667	70.8571	124	0.5375	0.7606
0.3667	72.0	126	0.5797	0.7113
0.3667	72.5714	127	0.6182	0.6690
0.3667	73.7143	129	0.6497	0.6690
0.3357	74.8571	131	0.6432	0.6831
0.3357	76.0	133	0.6772	0.6620
0.3357	76.5714	134	0.6395	0.6479
0.3357	77.7143	136	0.5895	0.7042
0.3357	78.8571	138	0.5921	0.6972
0.3415	80.0	140	0.5618	0.7254
0.3415	80.5714	141	0.5697	0.7183
0.3415	81.7143	143	0.6535	0.6197
0.3415	82.8571	145	0.6627	0.6338
0.3415	84.0	147	0.6194	0.6761
0.3415	84.5714	148	0.6301	0.6901
0.3296	85.7143	150	0.6436	0.6690
0.3296	86.8571	152	0.6348	0.6831
0.3296	88.0	154	0.6704	0.6479
0.3296	88.5714	155	0.7190	0.6338
0.3296	89.7143	157	0.7064	0.6338
0.3296	90.8571	159	0.6291	0.6549
0.3296	92.0	161	0.6933	0.6197
0.3296	92.5714	162	0.7115	0.6197
0.3296	93.7143	164	0.6229	0.6690
0.3296	94.8571	166	0.5727	0.7183
0.3296	96.0	168	0.5965	0.6901
0.3296	96.5714	169	0.6433	0.6690
0.3174	97.7143	171	0.6634	0.6408
0.3174	98.8571	173	0.6166	0.6549
0.3174	100.0	175	0.5896	0.6972
0.3174	100.5714	176	0.6092	0.6549
0.3174	101.7143	178	0.6022	0.6549
0.3309	102.8571	180	0.5928	0.6761
0.3309	104.0	182	0.6327	0.6408
0.3309	104.5714	183	0.6490	0.6338
0.3309	105.7143	185	0.6155	0.6479
0.3309	106.8571	187	0.6225	0.6620
0.3309	108.0	189	0.6732	0.6408
0.3124	108.5714	190	0.6808	0.6408
0.3124	109.7143	192	0.6585	0.6479
0.3124	110.8571	194	0.6122	0.6761
0.3124	112.0	196	0.6510	0.6549
0.3124	112.5714	197	0.7099	0.6408
0.3124	113.7143	199	0.7192	0.6338
0.3158	114.8571	201	0.6186	0.6901
0.3158	116.0	203	0.6071	0.7042
0.3158	116.5714	204	0.6419	0.6831
0.3158	117.7143	206	0.6679	0.6549
0.3158	118.8571	208	0.6825	0.6268
0.3026	120.0	210	0.6091	0.6972
0.3026	120.5714	211	0.5861	0.7394
0.3026	121.7143	213	0.6037	0.7113
0.3026	122.8571	215	0.6315	0.6761
0.3026	124.0	217	0.6328	0.6690
0.3026	124.5714	218	0.6187	0.6831
0.2968	125.7143	220	0.5843	0.7394
0.2968	126.8571	222	0.6126	0.7042
0.2968	128.0	224	0.6785	0.6549
0.2968	128.5714	225	0.6706	0.6479
0.2968	129.7143	227	0.6070	0.7113
0.2968	130.8571	229	0.5984	0.7254
0.294	132.0	231	0.6533	0.6620
0.294	132.5714	232	0.6802	0.6408
0.294	133.7143	234	0.6804	0.6408
0.294	134.8571	236	0.6228	0.7042
0.294	136.0	238	0.5849	0.7676
0.294	136.5714	239	0.5874	0.7676
0.3009	137.7143	241	0.6230	0.7042
0.3009	138.8571	243	0.6641	0.6549
0.3009	140.0	245	0.6435	0.6972
0.3009	140.5714	246	0.6134	0.7254
0.3009	141.7143	248	0.6063	0.7394
0.2873	142.8571	250	0.6347	0.6972
0.2873	144.0	252	0.6992	0.6690
0.2873	144.5714	253	0.7137	0.6408
0.2873	145.7143	255	0.6738	0.6690
0.2873	146.8571	257	0.6321	0.7113
0.2873	148.0	259	0.6135	0.7183
0.2821	148.5714	260	0.6195	0.7113
0.2821	149.7143	262	0.6544	0.6761
0.2821	150.8571	264	0.6464	0.6831
0.2821	152.0	266	0.6087	0.7324
0.2821	152.5714	267	0.6000	0.7394
0.2821	153.7143	269	0.6170	0.7113
0.3017	154.8571	271	0.6674	0.6831
0.3017	156.0	273	0.7137	0.6338
0.3017	156.5714	274	0.7014	0.6479
0.3017	157.7143	276	0.6091	0.7254
0.3017	158.8571	278	0.5626	0.7676
0.2857	160.0	280	0.5685	0.7606
0.2857	160.5714	281	0.5941	0.7113
0.2857	161.7143	283	0.6219	0.7113
0.2857	162.8571	285	0.6283	0.7113
0.2857	164.0	287	0.6314	0.7042
0.2857	164.5714	288	0.6369	0.6972
0.2819	165.7143	290	0.6446	0.6972
0.2819	166.8571	292	0.6541	0.6901
0.2819	168.0	294	0.6286	0.7183
0.2819	168.5714	295	0.6064	0.7183
0.2819	169.7143	297	0.5995	0.7254
0.2819	170.8571	299	0.6431	0.7254
0.2744	172.0	301	0.6797	0.6901
0.2744	172.5714	302	0.6716	0.6972
0.2744	173.7143	304	0.6510	0.7254
0.2744	174.8571	306	0.6362	0.7465
0.2744	176.0	308	0.6158	0.7606
0.2744	176.5714	309	0.6099	0.7676
0.2867	177.7143	311	0.6112	0.7535
0.2867	178.8571	313	0.6035	0.7465
0.2867	180.0	315	0.5816	0.7676
0.2867	180.5714	316	0.5818	0.7676
0.2867	181.7143	318	0.6078	0.7676
0.2883	182.8571	320	0.6083	0.7535
0.2883	184.0	322	0.5928	0.7465
0.2883	184.5714	323	0.5862	0.7535
0.2883	185.7143	325	0.5625	0.7676
0.2883	186.8571	327	0.5580	0.7817
0.2883	188.0	329	0.5945	0.7535
0.2852	188.5714	330	0.6321	0.6972
0.2852	189.7143	332	0.6650	0.6620
0.2852	190.8571	334	0.6612	0.6690
0.2852	192.0	336	0.6455	0.6761
0.2852	192.5714	337	0.6290	0.7113
0.2852	193.7143	339	0.6036	0.7394
0.2941	194.8571	341	0.5879	0.7535
0.2941	196.0	343	0.6135	0.7254
0.2941	196.5714	344	0.6295	0.7113
0.2941	197.7143	346	0.6445	0.6831
0.2941	198.8571	348	0.6591	0.6690
0.2692	200.0	350	0.6557	0.6831
0.2692	200.5714	351	0.6485	0.7113
0.2692	201.7143	353	0.6520	0.7183
0.2692	202.8571	355	0.6673	0.7113
0.2692	204.0	357	0.6814	0.7183
0.2692	204.5714	358	0.6694	0.7113
0.2666	205.7143	360	0.6350	0.7254
0.2666	206.8571	362	0.6091	0.7465
0.2666	208.0	364	0.6222	0.7394
0.2666	208.5714	365	0.6363	0.7394
0.2666	209.7143	367	0.6398	0.7394
0.2666	210.8571	369	0.6555	0.7254
0.2745	212.0	371	0.6555	0.7254
0.2745	212.5714	372	0.6467	0.7394
0.2745	213.7143	374	0.6216	0.7606
0.2745	214.8571	376	0.6066	0.7676
0.2745	216.0	378	0.6083	0.7606
0.2745	216.5714	379	0.6152	0.7535
0.2578	217.7143	381	0.6162	0.7535
0.2578	218.8571	383	0.6097	0.7535
0.2578	220.0	385	0.6003	0.7465
0.2578	220.5714	386	0.6064	0.7535
0.2578	221.7143	388	0.6182	0.7535
0.2637	222.8571	390	0.6465	0.7465
0.2637	224.0	392	0.6461	0.7535
0.2637	224.5714	393	0.6352	0.7535
0.2637	225.7143	395	0.6018	0.7606
0.2637	226.8571	397	0.5855	0.7746
0.2637	228.0	399	0.5916	0.7606
0.2696	228.5714	400	0.6031	0.7606
0.2696	229.7143	402	0.6308	0.7606
0.2696	230.8571	404	0.6435	0.7465
0.2696	232.0	406	0.6325	0.7465
0.2696	232.5714	407	0.6212	0.7535
0.2696	233.7143	409	0.5986	0.7535
0.2697	234.8571	411	0.5964	0.7465
0.2697	236.0	413	0.5950	0.7465
0.2697	236.5714	414	0.5986	0.7465
0.2697	237.7143	416	0.6066	0.7535
0.2697	238.8571	418	0.6035	0.7535
0.2659	240.0	420	0.6039	0.7535
0.2659	240.5714	421	0.6004	0.7535
0.2659	241.7143	423	0.6001	0.7535
0.2659	242.8571	425	0.5941	0.7465
0.2659	244.0	427	0.5942	0.7394
0.2659	244.5714	428	0.5972	0.7465
0.2529	245.7143	430	0.6077	0.7535
0.2529	246.8571	432	0.6173	0.7465
0.2529	248.0	434	0.6129	0.7606
0.2529	248.5714	435	0.6099	0.7606
0.2529	249.7143	437	0.6005	0.7606
0.2529	250.8571	439	0.5920	0.7606
0.261	252.0	441	0.5946	0.7606
0.261	252.5714	442	0.5992	0.7606
0.261	253.7143	444	0.6142	0.7606
0.261	254.8571	446	0.6289	0.7465
0.261	256.0	448	0.6316	0.7465
0.261	256.5714	449	0.6302	0.7535
0.2675	257.7143	451	0.6241	0.7535
0.2675	258.8571	453	0.6129	0.7535
0.2675	260.0	455	0.6066	0.7465
0.2675	260.5714	456	0.6061	0.7465
0.2675	261.7143	458	0.6098	0.7535
0.2737	262.8571	460	0.6172	0.7394
0.2737	264.0	462	0.6274	0.7324
0.2737	264.5714	463	0.6298	0.7324
0.2737	265.7143	465	0.6296	0.7324
0.2737	266.8571	467	0.6285	0.7324
0.2737	268.0	469	0.6265	0.7324
0.2504	268.5714	470	0.6274	0.7465
0.2504	269.7143	472	0.6286	0.7394
0.2504	270.8571	474	0.6236	0.7465
0.2504	272.0	476	0.6178	0.7465
0.2504	272.5714	477	0.6164	0.7465
0.2504	273.7143	479	0.6161	0.7465
0.2539	274.8571	481	0.6193	0.7465
0.2539	276.0	483	0.6236	0.7394
0.2539	276.5714	484	0.6258	0.7394
0.2539	277.7143	486	0.6308	0.7394
0.2539	278.8571	488	0.6349	0.7394
0.2508	280.0	490	0.6352	0.7394
0.2508	280.5714	491	0.6346	0.7394
0.2508	281.7143	493	0.6336	0.7394
0.2508	282.8571	495	0.6331	0.7394
0.2508	284.0	497	0.6324	0.7394
0.2508	284.5714	498	0.6319	0.7394
0.2393	285.7143	500	0.6316	0.7394

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1

Melo1512/vit-msn-small-beta-fia-manually-enhanced_test_2

vit-msn-small-beta-fia-manually-enhanced_test_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model