This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2838
Accuracy: 0.8696

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: 2e-05
train_batch_size: 10
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	11	1.9719	0.6522
No log	2.0	22	1.6381	0.6522
No log	3.0	33	1.3958	0.6522
No log	4.0	44	1.2541	0.6522
No log	5.0	55	1.1207	0.6957
No log	6.0	66	1.0262	0.8261
No log	7.0	77	0.9421	0.8261
No log	8.0	88	0.9031	0.8261
No log	9.0	99	0.8398	0.8261
No log	10.0	110	0.7975	0.8261
No log	11.0	121	0.7547	0.8696
No log	12.0	132	0.7451	0.8696
No log	13.0	143	0.7017	0.8696
No log	14.0	154	0.6789	0.8696
No log	15.0	165	0.6688	0.8696
No log	16.0	176	0.6809	0.8696
No log	17.0	187	0.6342	0.8696
No log	18.0	198	0.6437	0.8696
No log	19.0	209	0.5902	0.8696
No log	20.0	220	0.5874	0.8696
No log	21.0	231	0.6042	0.8696
No log	22.0	242	0.5682	0.8696
No log	23.0	253	0.5395	0.8696
No log	24.0	264	0.5487	0.8696
No log	25.0	275	0.5239	0.8696
No log	26.0	286	0.5436	0.8696
No log	27.0	297	0.5169	0.8696
No log	28.0	308	0.5189	0.8696
No log	29.0	319	0.5314	0.8261
No log	30.0	330	0.4707	0.8696
No log	31.0	341	0.5169	0.8261
No log	32.0	352	0.5229	0.8696
No log	33.0	363	0.4598	0.8696
No log	34.0	374	0.4911	0.8696
No log	35.0	385	0.4516	0.8696
No log	36.0	396	0.4121	0.9130
No log	37.0	407	0.4875	0.8696
No log	38.0	418	0.4147	0.9130
No log	39.0	429	0.5118	0.8696
No log	40.0	440	0.4266	0.8696
No log	41.0	451	0.4114	0.8696
No log	42.0	462	0.4549	0.8261
No log	43.0	473	0.3795	0.9565
No log	44.0	484	0.4286	0.8696
No log	45.0	495	0.4409	0.8696
0.6437	46.0	506	0.4099	0.8696
0.6437	47.0	517	0.4075	0.9130
0.6437	48.0	528	0.3886	0.9130
0.6437	49.0	539	0.3900	0.8696
0.6437	50.0	550	0.3947	0.8696
0.6437	51.0	561	0.3676	0.8696
0.6437	52.0	572	0.3560	0.9130
0.6437	53.0	583	0.4100	0.8696
0.6437	54.0	594	0.4078	0.8696
0.6437	55.0	605	0.4357	0.8696
0.6437	56.0	616	0.3815	0.8696
0.6437	57.0	627	0.4172	0.8696
0.6437	58.0	638	0.4781	0.8696
0.6437	59.0	649	0.3847	0.8696
0.6437	60.0	660	0.3260	0.9130
0.6437	61.0	671	0.3578	0.8696
0.6437	62.0	682	0.3096	0.9130
0.6437	63.0	693	0.2946	0.9130
0.6437	64.0	704	0.3383	0.8696
0.6437	65.0	715	0.3748	0.8696
0.6437	66.0	726	0.3199	0.9130
0.6437	67.0	737	0.3761	0.8696
0.6437	68.0	748	0.3332	0.8696
0.6437	69.0	759	0.2815	0.9130
0.6437	70.0	770	0.3236	0.8696
0.6437	71.0	781	0.2962	0.9130
0.6437	72.0	792	0.3075	0.9130
0.6437	73.0	803	0.3461	0.8696
0.6437	74.0	814	0.3138	0.9130
0.6437	75.0	825	0.3043	0.9130
0.6437	76.0	836	0.2967	0.9130
0.6437	77.0	847	0.3008	0.9130
0.6437	78.0	858	0.2856	0.9130
0.6437	79.0	869	0.2939	0.9130
0.6437	80.0	880	0.3491	0.9130
0.6437	81.0	891	0.3049	0.9130
0.6437	82.0	902	0.3577	0.8696
0.6437	83.0	913	0.3369	0.8696
0.6437	84.0	924	0.2952	0.9130
0.6437	85.0	935	0.2881	0.9130
0.6437	86.0	946	0.3349	0.8696
0.6437	87.0	957	0.3025	0.9130
0.6437	88.0	968	0.2943	0.8696
0.6437	89.0	979	0.3035	0.9130
0.6437	90.0	990	0.2599	0.9130
0.1677	91.0	1001	0.3061	0.8696
0.1677	92.0	1012	0.4316	0.8261
0.1677	93.0	1023	0.3431	0.8696
0.1677	94.0	1034	0.3246	0.8696
0.1677	95.0	1045	0.3256	0.8696
0.1677	96.0	1056	0.2846	0.9130
0.1677	97.0	1067	0.3077	0.8696
0.1677	98.0	1078	0.2988	0.9130
0.1677	99.0	1089	0.2957	0.9130
0.1677	100.0	1100	0.2983	0.9130
0.1677	101.0	1111	0.2908	0.8696
0.1677	102.0	1122	0.2715	0.9130
0.1677	103.0	1133	0.3208	0.9130
0.1677	104.0	1144	0.3622	0.8261
0.1677	105.0	1155	0.3314	0.8696
0.1677	106.0	1166	0.3226	0.9130
0.1677	107.0	1177	0.3009	0.9565
0.1677	108.0	1188	0.3162	0.9130
0.1677	109.0	1199	0.2927	0.9565
0.1677	110.0	1210	0.2434	0.9130
0.1677	111.0	1221	0.3389	0.8696
0.1677	112.0	1232	0.3686	0.8696
0.1677	113.0	1243	0.3192	0.9130
0.1677	114.0	1254	0.2720	0.8696
0.1677	115.0	1265	0.2955	0.8696
0.1677	116.0	1276	0.3318	0.9130
0.1677	117.0	1287	0.3248	0.9130
0.1677	118.0	1298	0.3115	0.8696
0.1677	119.0	1309	0.2711	0.9130
0.1677	120.0	1320	0.2592	0.8696
0.1677	121.0	1331	0.2830	0.8696
0.1677	122.0	1342	0.2956	0.9130
0.1677	123.0	1353	0.3158	0.9130
0.1677	124.0	1364	0.3328	0.8696
0.1677	125.0	1375	0.3487	0.8696
0.1677	126.0	1386	0.3375	0.8696
0.1677	127.0	1397	0.3488	0.8696
0.1677	128.0	1408	0.3377	0.8696
0.1677	129.0	1419	0.3295	0.8696
0.1677	130.0	1430	0.3198	0.8696
0.1677	131.0	1441	0.2813	0.9130
0.1677	132.0	1452	0.2730	0.9130
0.1677	133.0	1463	0.2861	0.8696
0.1677	134.0	1474	0.3158	0.8696
0.1677	135.0	1485	0.3229	0.8696
0.1677	136.0	1496	0.3169	0.8696
0.1074	137.0	1507	0.3215	0.8696
0.1074	138.0	1518	0.3039	0.8696
0.1074	139.0	1529	0.2803	0.9130
0.1074	140.0	1540	0.2707	0.9130
0.1074	141.0	1551	0.2601	0.9130
0.1074	142.0	1562	0.2599	0.9130
0.1074	143.0	1573	0.2647	0.9130
0.1074	144.0	1584	0.2697	0.9130
0.1074	145.0	1595	0.2738	0.9130
0.1074	146.0	1606	0.2759	0.9130
0.1074	147.0	1617	0.2797	0.9130
0.1074	148.0	1628	0.2798	0.9130
0.1074	149.0	1639	0.2829	0.8696
0.1074	150.0	1650	0.2838	0.8696

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

ebayes/test-fern-amazonas

ebayes/tree_crown_model-test23

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ebayes/
test-fern-amazonas