smids_5x_deit_small_rms_001_fold5

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: 0.9266
• Accuracy: 0.7933

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
0.8478	1.0	375	0.8288	0.5567
0.8902	2.0	750	0.8202	0.5483
0.8343	3.0	1125	0.7711	0.65
0.8476	4.0	1500	0.8002	0.5683
0.7349	5.0	1875	0.7526	0.62
0.7146	6.0	2250	0.7401	0.645
0.6924	7.0	2625	0.7402	0.6467
0.8092	8.0	3000	0.7366	0.6267
0.7302	9.0	3375	0.7094	0.67
0.6674	10.0	3750	0.6785	0.6733
0.7108	11.0	4125	0.6584	0.6967
0.5797	12.0	4500	0.7184	0.68
0.6078	13.0	4875	0.6814	0.6767
0.6457	14.0	5250	0.6261	0.72
0.6638	15.0	5625	0.5980	0.7217
0.5829	16.0	6000	0.5841	0.7517
0.5785	17.0	6375	0.5759	0.7283
0.5879	18.0	6750	0.5909	0.7233
0.5859	19.0	7125	0.6185	0.7183
0.5569	20.0	7500	0.5506	0.745
0.5537	21.0	7875	0.5606	0.7617
0.5092	22.0	8250	0.5522	0.7483
0.61	23.0	8625	0.6297	0.7383
0.5833	24.0	9000	0.5399	0.7667
0.5315	25.0	9375	0.5551	0.7517
0.5313	26.0	9750	0.5176	0.7717
0.5327	27.0	10125	0.5547	0.775
0.4821	28.0	10500	0.5221	0.77
0.5852	29.0	10875	0.4983	0.7717
0.4777	30.0	11250	0.5766	0.75
0.4511	31.0	11625	0.5104	0.7733
0.5002	32.0	12000	0.5870	0.76
0.465	33.0	12375	0.4942	0.7917
0.4934	34.0	12750	0.5302	0.7783
0.4217	35.0	13125	0.5314	0.7883
0.3994	36.0	13500	0.5461	0.7917
0.3823	37.0	13875	0.5187	0.7933
0.3965	38.0	14250	0.5803	0.7917
0.3576	39.0	14625	0.5564	0.79
0.3853	40.0	15000	0.5425	0.8033
0.3694	41.0	15375	0.5885	0.7967
0.3496	42.0	15750	0.6131	0.7967
0.3293	43.0	16125	0.6330	0.8033
0.2565	44.0	16500	0.6562	0.795
0.3188	45.0	16875	0.7306	0.7933
0.2833	46.0	17250	0.8042	0.7917
0.2208	47.0	17625	0.7887	0.79
0.1436	48.0	18000	0.8206	0.7933
0.1521	49.0	18375	0.8762	0.8083
0.1603	50.0	18750	0.9266	0.7933

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2