hushem_5x_deit_tiny_rms_001_fold2

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: 4.2103
• Accuracy: 0.4667

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
1.9281	1.0	27	1.5117	0.2444
1.5142	2.0	54	1.4322	0.2444
1.4521	3.0	81	1.4347	0.2667
1.4849	4.0	108	1.6669	0.2444
1.4295	5.0	135	1.4219	0.2444
1.5039	6.0	162	1.4488	0.2444
1.4071	7.0	189	1.5265	0.2667
1.3353	8.0	216	1.4657	0.2
1.2509	9.0	243	1.4974	0.2667
1.2656	10.0	270	1.3879	0.2667
1.1653	11.0	297	1.5762	0.3333
1.1362	12.0	324	1.6646	0.4
1.2223	13.0	351	1.6454	0.1556
1.027	14.0	378	1.7430	0.2222
1.0093	15.0	405	1.8846	0.3778
0.9584	16.0	432	2.4104	0.1333
0.9432	17.0	459	2.0298	0.2667
0.9525	18.0	486	2.0687	0.3111
0.8065	19.0	513	2.3917	0.1778
0.8858	20.0	540	1.9861	0.2222
0.761	21.0	567	2.1617	0.3111
0.7456	22.0	594	2.6510	0.2667
0.7215	23.0	621	2.5639	0.4
0.7957	24.0	648	2.1510	0.3111
0.6858	25.0	675	2.2884	0.5111
0.6662	26.0	702	2.5211	0.4667
0.6301	27.0	729	2.5983	0.4667
0.676	28.0	756	2.4047	0.3778
0.5592	29.0	783	2.9746	0.4444
0.5758	30.0	810	2.5122	0.3778
0.5241	31.0	837	2.9556	0.4222
0.5249	32.0	864	2.6136	0.4889
0.527	33.0	891	2.5736	0.4222
0.5079	34.0	918	2.8504	0.5333
0.4469	35.0	945	3.2380	0.4
0.4391	36.0	972	3.1267	0.4444
0.3925	37.0	999	3.3000	0.4667
0.3719	38.0	1026	3.1308	0.5111
0.3137	39.0	1053	3.2238	0.4889
0.2551	40.0	1080	3.3279	0.5333
0.2638	41.0	1107	3.4382	0.5111
0.2004	42.0	1134	3.7709	0.4667
0.1877	43.0	1161	3.7286	0.4222
0.1439	44.0	1188	3.9363	0.4889
0.1405	45.0	1215	4.0904	0.4667
0.1084	46.0	1242	4.2188	0.4889
0.0629	47.0	1269	4.1702	0.5111
0.0462	48.0	1296	4.2158	0.4667
0.0313	49.0	1323	4.2103	0.4667
0.0292	50.0	1350	4.2103	0.4667

Framework versions

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