beit-base-patch16-224-hasta-55-fold1

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: 0.9506
• 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	1.1111	0.4722
No log	1.7143	3	1.2067	0.2222
No log	2.8571	5	1.1273	0.3889
No log	4.0	7	1.0384	0.4444
No log	4.5714	8	1.1017	0.4167
1.0728	5.7143	10	1.1338	0.3889
1.0728	6.8571	12	0.9428	0.5
1.0728	8.0	14	1.0236	0.5
1.0728	8.5714	15	0.9506	0.6111
1.0728	9.7143	17	0.9171	0.6111
1.0728	10.8571	19	1.1098	0.4722
0.9628	12.0	21	0.9111	0.6111
0.9628	12.5714	22	0.9954	0.5556
0.9628	13.7143	24	1.1014	0.5833
0.9628	14.8571	26	0.9581	0.5556
0.9628	16.0	28	1.0735	0.5278
0.9628	16.5714	29	1.1480	0.5556
0.833	17.7143	31	1.0334	0.5833
0.833	18.8571	33	0.9554	0.5833
0.833	20.0	35	1.2890	0.5833
0.833	20.5714	36	1.3330	0.5
0.833	21.7143	38	1.0786	0.5556
0.7274	22.8571	40	1.1173	0.5
0.7274	24.0	42	1.1471	0.5
0.7274	24.5714	43	1.1779	0.5
0.7274	25.7143	45	1.2391	0.5278
0.7274	26.8571	47	1.2273	0.5556
0.7274	28.0	49	1.1947	0.4722
0.6321	28.5714	50	1.2452	0.5278
0.6321	29.7143	52	1.1783	0.4722
0.6321	30.8571	54	1.2355	0.5278
0.6321	32.0	56	1.1921	0.5
0.6321	32.5714	57	1.1682	0.5
0.6321	33.7143	59	1.2613	0.5556
0.5586	34.8571	61	1.2683	0.5833
0.5586	36.0	63	1.2235	0.5278
0.5586	36.5714	64	1.2214	0.5833
0.5586	37.7143	66	1.0498	0.5556
0.5586	38.8571	68	1.0474	0.5833
0.4814	40.0	70	1.2388	0.5833
0.4814	40.5714	71	1.2826	0.5556
0.4814	41.7143	73	1.1847	0.5556
0.4814	42.8571	75	1.0760	0.5278
0.4814	44.0	77	1.0788	0.5833
0.4814	44.5714	78	1.0894	0.5833
0.4638	45.7143	80	1.2232	0.6111
0.4638	46.8571	82	1.2743	0.5833
0.4638	48.0	84	1.2030	0.6111
0.4638	48.5714	85	1.1366	0.5833
0.4638	49.7143	87	1.0802	0.5833
0.4638	50.8571	89	1.1044	0.6111
0.3833	52.0	91	1.1679	0.6111
0.3833	52.5714	92	1.1905	0.6111
0.3833	53.7143	94	1.2243	0.6111
0.3833	54.8571	96	1.2445	0.5833
0.3833	56.0	98	1.2267	0.6111
0.3833	56.5714	99	1.2180	0.6111
0.3405	57.1429	100	1.2154	0.6111

Framework versions

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