hushem_5x_deit_tiny_sgd_001_fold3

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

• Loss: 1.0019
• Accuracy: 0.5581

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
1.5059	1.0	28	1.5596	0.3023
1.3905	2.0	56	1.4722	0.3023
1.3398	3.0	84	1.4240	0.3023
1.3159	4.0	112	1.3834	0.3488
1.2162	5.0	140	1.3509	0.3721
1.2552	6.0	168	1.3214	0.3721
1.2492	7.0	196	1.3011	0.3721
1.2051	8.0	224	1.2801	0.3953
1.1592	9.0	252	1.2612	0.3953
1.1549	10.0	280	1.2440	0.3953
1.1103	11.0	308	1.2292	0.3953
1.1084	12.0	336	1.2159	0.3953
1.0301	13.0	364	1.2021	0.3953
1.0455	14.0	392	1.1867	0.4419
1.0462	15.0	420	1.1750	0.4419
1.0114	16.0	448	1.1653	0.4419
0.9862	17.0	476	1.1504	0.4651
0.9557	18.0	504	1.1422	0.4651
0.9635	19.0	532	1.1339	0.4651
0.9341	20.0	560	1.1250	0.4419
0.9142	21.0	588	1.1124	0.4651
0.9058	22.0	616	1.1036	0.4651
0.9242	23.0	644	1.0941	0.4884
0.8383	24.0	672	1.0875	0.5349
0.9017	25.0	700	1.0842	0.5116
0.8456	26.0	728	1.0767	0.5349
0.8584	27.0	756	1.0674	0.5349
0.8615	28.0	784	1.0594	0.5349
0.8302	29.0	812	1.0523	0.5581
0.8218	30.0	840	1.0467	0.5581
0.7666	31.0	868	1.0425	0.5581
0.8374	32.0	896	1.0358	0.5581
0.8102	33.0	924	1.0386	0.5581
0.803	34.0	952	1.0300	0.5581
0.8593	35.0	980	1.0290	0.5349
0.781	36.0	1008	1.0257	0.5349
0.769	37.0	1036	1.0205	0.5349
0.7568	38.0	1064	1.0221	0.5349
0.7521	39.0	1092	1.0159	0.5349
0.7774	40.0	1120	1.0130	0.5349
0.7906	41.0	1148	1.0102	0.5349
0.7478	42.0	1176	1.0092	0.5349
0.7422	43.0	1204	1.0064	0.5349
0.7561	44.0	1232	1.0041	0.5349
0.7139	45.0	1260	1.0022	0.5349
0.7352	46.0	1288	1.0021	0.5581
0.7448	47.0	1316	1.0021	0.5581
0.7592	48.0	1344	1.0020	0.5581
0.7313	49.0	1372	1.0019	0.5581
0.6642	50.0	1400	1.0019	0.5581

Framework versions

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