hushem_5x_deit_small_rms_001_fold1

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: 4.9688
• Accuracy: 0.4444

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
1.7253	1.0	27	1.4539	0.2444
1.4329	2.0	54	1.3827	0.3556
1.413	3.0	81	1.4287	0.2667
1.2575	4.0	108	1.2729	0.4222
1.2908	5.0	135	1.8913	0.3333
1.1882	6.0	162	1.1330	0.5111
1.0961	7.0	189	1.6635	0.3778
1.0705	8.0	216	1.0816	0.5556
0.8596	9.0	243	2.1258	0.4
0.8047	10.0	270	1.2784	0.4444
0.7923	11.0	297	2.1314	0.3778
0.7354	12.0	324	1.5632	0.3778
0.7076	13.0	351	1.6923	0.4
0.7272	14.0	378	1.4002	0.4222
0.6359	15.0	405	1.6646	0.4
0.5977	16.0	432	1.6603	0.4444
0.6463	17.0	459	1.5891	0.4444
0.6624	18.0	486	1.8543	0.4
0.5726	19.0	513	1.5545	0.5111
0.5713	20.0	540	1.7099	0.4
0.5626	21.0	567	1.6364	0.4
0.5358	22.0	594	1.8888	0.4667
0.5334	23.0	621	1.9170	0.4667
0.4645	24.0	648	2.0287	0.4222
0.5514	25.0	675	1.5224	0.4889
0.5254	26.0	702	2.4633	0.3556
0.441	27.0	729	1.7933	0.4222
0.3855	28.0	756	2.4673	0.4222
0.4099	29.0	783	2.6353	0.4222
0.4294	30.0	810	2.2588	0.4444
0.3329	31.0	837	2.3858	0.4
0.3787	32.0	864	2.2861	0.3333
0.3457	33.0	891	2.1705	0.4222
0.2509	34.0	918	2.4731	0.4222
0.1898	35.0	945	2.9376	0.3111
0.197	36.0	972	3.2201	0.4222
0.1255	37.0	999	2.5816	0.5333
0.1258	38.0	1026	3.6398	0.4
0.1837	39.0	1053	3.2179	0.4222
0.0881	40.0	1080	3.5990	0.4444
0.0547	41.0	1107	4.2943	0.4222
0.0315	42.0	1134	4.0730	0.4222
0.0187	43.0	1161	4.2944	0.4667
0.0043	44.0	1188	4.5081	0.4667
0.0016	45.0	1215	4.8996	0.4444
0.0009	46.0	1242	4.8993	0.4444
0.0009	47.0	1269	4.9469	0.4444
0.0007	48.0	1296	4.9681	0.4444
0.0006	49.0	1323	4.9688	0.4444
0.0006	50.0	1350	4.9688	0.4444

Framework versions

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