beit-base-patch16-224-hasta-55-fold5

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

• Loss: 1.4708
• Accuracy: 0.6111

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.5714	1	1.2240	0.3056
No log	1.7143	3	1.2263	0.3056
No log	2.8571	5	1.2222	0.3889
No log	4.0	7	1.1690	0.3889
No log	4.5714	8	1.1691	0.3889
1.1249	5.7143	10	1.0999	0.3889
1.1249	6.8571	12	1.1605	0.4167
1.1249	8.0	14	1.1912	0.4167
1.1249	8.5714	15	1.1771	0.3889
1.1249	9.7143	17	1.2370	0.4722
1.1249	10.8571	19	1.2607	0.5
0.9274	12.0	21	1.2756	0.4722
0.9274	12.5714	22	1.2208	0.4722
0.9274	13.7143	24	1.3705	0.5
0.9274	14.8571	26	1.2191	0.5278
0.9274	16.0	28	1.3502	0.5278
0.9274	16.5714	29	1.2628	0.5278
0.7889	17.7143	31	1.0868	0.5
0.7889	18.8571	33	1.3983	0.5
0.7889	20.0	35	1.2537	0.5556
0.7889	20.5714	36	1.1540	0.4722
0.7889	21.7143	38	1.2135	0.5556
0.7027	22.8571	40	1.4271	0.5
0.7027	24.0	42	1.1828	0.5
0.7027	24.5714	43	1.2126	0.4444
0.7027	25.7143	45	1.4980	0.5556
0.7027	26.8571	47	1.3495	0.5556
0.7027	28.0	49	1.1969	0.5278
0.6037	28.5714	50	1.2063	0.5556
0.6037	29.7143	52	1.3115	0.5833
0.6037	30.8571	54	1.1726	0.5278
0.6037	32.0	56	1.1872	0.5556
0.6037	32.5714	57	1.2399	0.5556
0.6037	33.7143	59	1.2566	0.5278
0.5147	34.8571	61	1.1848	0.5278
0.5147	36.0	63	1.2614	0.5556
0.5147	36.5714	64	1.3975	0.5556
0.5147	37.7143	66	1.4708	0.6111
0.5147	38.8571	68	1.3233	0.5833
0.4004	40.0	70	1.2994	0.5556
0.4004	40.5714	71	1.3582	0.5278
0.4004	41.7143	73	1.3577	0.5278
0.4004	42.8571	75	1.1985	0.5833
0.4004	44.0	77	1.1448	0.5556
0.4004	44.5714	78	1.1714	0.6111
0.4323	45.7143	80	1.3707	0.6111
0.4323	46.8571	82	1.5477	0.5833
0.4323	48.0	84	1.4254	0.5833
0.4323	48.5714	85	1.3031	0.5833
0.4323	49.7143	87	1.1843	0.6111
0.4323	50.8571	89	1.1835	0.6111
0.3568	52.0	91	1.2399	0.6111
0.3568	52.5714	92	1.2606	0.6111
0.3568	53.7143	94	1.2997	0.5833
0.3568	54.8571	96	1.3184	0.5833
0.3568	56.0	98	1.3294	0.5833
0.3568	56.5714	99	1.3337	0.5833
0.3308	57.1429	100	1.3367	0.5833

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1