hushem_5x_deit_small_sgd_001_fold3

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.1692
• Accuracy: 0.4186

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• 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
1.4612	1.0	28	1.5118	0.2558
1.3805	2.0	56	1.4497	0.2093
1.3041	3.0	84	1.4123	0.2093
1.3096	4.0	112	1.3866	0.2558
1.3306	5.0	140	1.3677	0.2558
1.2628	6.0	168	1.3531	0.2791
1.2467	7.0	196	1.3404	0.2791
1.2266	8.0	224	1.3306	0.3023
1.194	9.0	252	1.3214	0.3023
1.1663	10.0	280	1.3122	0.2791
1.1821	11.0	308	1.3053	0.3023
1.1428	12.0	336	1.2975	0.3023
1.1221	13.0	364	1.2914	0.3023
1.0981	14.0	392	1.2852	0.3023
1.1081	15.0	420	1.2792	0.3488
1.0434	16.0	448	1.2755	0.3488
1.0661	17.0	476	1.2676	0.3721
1.0349	18.0	504	1.2624	0.3721
1.0251	19.0	532	1.2563	0.3721
0.9878	20.0	560	1.2508	0.3721
1.0276	21.0	588	1.2459	0.3721
0.9848	22.0	616	1.2419	0.3721
0.9825	23.0	644	1.2362	0.3721
0.9289	24.0	672	1.2312	0.3721
0.9221	25.0	700	1.2274	0.3721
0.9187	26.0	728	1.2222	0.3721
0.9248	27.0	756	1.2177	0.3953
0.9505	28.0	784	1.2135	0.3721
0.9022	29.0	812	1.2094	0.3721
0.8445	30.0	840	1.2060	0.3721
0.861	31.0	868	1.2023	0.3953
0.9005	32.0	896	1.1985	0.3953
0.8936	33.0	924	1.1956	0.3953
0.8469	34.0	952	1.1923	0.3953
0.9675	35.0	980	1.1892	0.3953
0.8615	36.0	1008	1.1869	0.3953
0.8944	37.0	1036	1.1838	0.3953
0.8374	38.0	1064	1.1820	0.3953
0.8431	39.0	1092	1.1797	0.3953
0.8451	40.0	1120	1.1776	0.3953
0.8614	41.0	1148	1.1756	0.3953
0.8347	42.0	1176	1.1741	0.3953
0.8159	43.0	1204	1.1724	0.4419
0.8229	44.0	1232	1.1713	0.4419
0.8046	45.0	1260	1.1703	0.4186
0.8129	46.0	1288	1.1698	0.4186
0.8156	47.0	1316	1.1693	0.4186
0.8259	48.0	1344	1.1692	0.4186
0.7943	49.0	1372	1.1692	0.4186
0.8288	50.0	1400	1.1692	0.4186

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0