smids_10x_beit_large_adamax_00001_fold2

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: 0.9196
• Accuracy: 0.9151

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
0.1587	1.0	750	0.2691	0.9101
0.0471	2.0	1500	0.3138	0.9135
0.0407	3.0	2250	0.4729	0.9118
0.0287	4.0	3000	0.5798	0.9068
0.012	5.0	3750	0.7233	0.9118
0.0109	6.0	4500	0.7175	0.9168
0.0017	7.0	5250	0.7940	0.9085
0.0129	8.0	6000	0.7917	0.9068
0.0001	9.0	6750	0.8466	0.9068
0.0033	10.0	7500	0.8662	0.9002
0.0001	11.0	8250	0.9262	0.9035
0.0005	12.0	9000	0.8648	0.9035
0.0001	13.0	9750	0.9176	0.9101
0.0001	14.0	10500	0.9531	0.8985
0.0002	15.0	11250	0.9250	0.9035
0.0418	16.0	12000	0.9389	0.9085
0.0	17.0	12750	0.9725	0.9035
0.0001	18.0	13500	0.9072	0.9101
0.0173	19.0	14250	0.9123	0.9151
0.0042	20.0	15000	0.9275	0.9068
0.0	21.0	15750	0.9111	0.9101
0.0243	22.0	16500	0.9348	0.9101
0.0002	23.0	17250	1.0125	0.9052
0.0002	24.0	18000	0.8943	0.9101
0.0	25.0	18750	1.0215	0.9035
0.0001	26.0	19500	0.9907	0.9085
0.0358	27.0	20250	0.9413	0.9101
0.0003	28.0	21000	0.8860	0.9201
0.0	29.0	21750	0.9273	0.9218
0.0	30.0	22500	0.9583	0.9068
0.0	31.0	23250	0.9280	0.9218
0.0	32.0	24000	0.9420	0.9168
0.0	33.0	24750	0.9244	0.9185
0.0	34.0	25500	0.9598	0.9085
0.0	35.0	26250	0.9576	0.9101
0.0	36.0	27000	0.9574	0.9101
0.0013	37.0	27750	0.9671	0.9101
0.0	38.0	28500	0.9627	0.9101
0.0	39.0	29250	0.9639	0.9118
0.0001	40.0	30000	0.9418	0.9118
0.0003	41.0	30750	0.9216	0.9135
0.0	42.0	31500	0.9226	0.9185
0.0	43.0	32250	0.9076	0.9218
0.0	44.0	33000	0.9133	0.9151
0.0006	45.0	33750	0.9164	0.9151
0.0	46.0	34500	0.9118	0.9168
0.0	47.0	35250	0.9173	0.9151
0.0	48.0	36000	0.9178	0.9101
0.0	49.0	36750	0.9196	0.9135
0.0	50.0	37500	0.9196	0.9151

Framework versions

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