smids_10x_beit_large_adamax_00001_fold3

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

• Loss: 0.7342
• Accuracy: 0.93

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: 1e-05
• 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
0.1234	1.0	750	0.2380	0.9133
0.0658	2.0	1500	0.2732	0.9317
0.0204	3.0	2250	0.3498	0.9217
0.0213	4.0	3000	0.4104	0.925
0.0054	5.0	3750	0.4509	0.9317
0.0002	6.0	4500	0.5343	0.9233
0.0104	7.0	5250	0.5450	0.9267
0.0001	8.0	6000	0.6214	0.9217
0.0002	9.0	6750	0.5669	0.9333
0.0	10.0	7500	0.5842	0.9233
0.0003	11.0	8250	0.5405	0.9267
0.0007	12.0	9000	0.6365	0.9233
0.0	13.0	9750	0.6437	0.9267
0.0006	14.0	10500	0.6868	0.92
0.0	15.0	11250	0.6484	0.93
0.0	16.0	12000	0.6945	0.925
0.0	17.0	12750	0.6473	0.925
0.0	18.0	13500	0.7329	0.9233
0.0	19.0	14250	0.6697	0.9283
0.0	20.0	15000	0.7054	0.9317
0.0	21.0	15750	0.7229	0.9267
0.0001	22.0	16500	0.6657	0.9267
0.0	23.0	17250	0.6845	0.925
0.0	24.0	18000	0.7071	0.9233
0.0	25.0	18750	0.7119	0.9267
0.0	26.0	19500	0.7250	0.9283
0.0	27.0	20250	0.7491	0.93
0.0	28.0	21000	0.7325	0.9267
0.0	29.0	21750	0.7225	0.93
0.0	30.0	22500	0.7702	0.93
0.0	31.0	23250	0.7702	0.93
0.0	32.0	24000	0.7279	0.93
0.0	33.0	24750	0.7215	0.9283
0.0	34.0	25500	0.7215	0.9267
0.0	35.0	26250	0.7456	0.9267
0.0	36.0	27000	0.7430	0.9267
0.0	37.0	27750	0.7363	0.9283
0.0	38.0	28500	0.7489	0.93
0.0	39.0	29250	0.7854	0.9267
0.0	40.0	30000	0.7378	0.9283
0.0	41.0	30750	0.7334	0.93
0.0	42.0	31500	0.7235	0.9333
0.0	43.0	32250	0.7203	0.93
0.0	44.0	33000	0.7319	0.9267
0.0	45.0	33750	0.7326	0.93
0.0	46.0	34500	0.7443	0.93
0.0	47.0	35250	0.7511	0.93
0.0	48.0	36000	0.7575	0.93
0.0	49.0	36750	0.7357	0.93
0.0	50.0	37500	0.7342	0.93

Framework versions

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