pogona-vitticeps-gender

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5663
• Accuracy: 0.7812

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: 4e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1028	1.0	2	1.1062	0.2812
1.0972	2.0	4	1.1082	0.3125
1.0793	3.0	6	1.0692	0.5312
1.0529	4.0	8	1.0578	0.625
1.0178	5.0	10	1.0288	0.625
0.9809	6.0	12	0.9988	0.6562
0.9422	7.0	14	0.9936	0.6562
0.8692	8.0	16	0.9761	0.625
0.8503	9.0	18	0.9326	0.5938
0.8128	10.0	20	0.9236	0.6562
0.777	11.0	22	0.8541	0.75
0.7407	12.0	24	0.8744	0.6562
0.692	13.0	26	0.8412	0.6875
0.6779	14.0	28	0.8611	0.6562
0.6261	15.0	30	0.8213	0.625
0.609	16.0	32	0.7389	0.7188
0.5905	17.0	34	0.7421	0.7188
0.5337	18.0	36	0.7651	0.6875
0.5091	19.0	38	0.7201	0.75
0.5178	20.0	40	0.7424	0.7188
0.4757	21.0	42	0.7573	0.6562
0.4548	22.0	44	0.7531	0.6562
0.4494	23.0	46	0.7185	0.7188
0.4627	24.0	48	0.6587	0.7188
0.423	25.0	50	0.6426	0.75
0.403	26.0	52	0.6525	0.75
0.3734	27.0	54	0.6733	0.75
0.38	28.0	56	0.6736	0.75
0.3702	29.0	58	0.7211	0.6875
0.3563	30.0	60	0.7263	0.6562
0.336	31.0	62	0.6676	0.6875
0.3131	32.0	64	0.6923	0.6875
0.3214	33.0	66	0.6137	0.75
0.3271	34.0	68	0.6708	0.8125
0.3253	35.0	70	0.5912	0.75
0.283	36.0	72	0.6332	0.7188
0.2874	37.0	74	0.6345	0.7188
0.2818	38.0	76	0.7593	0.6875
0.2774	39.0	78	0.6817	0.7188
0.2482	40.0	80	0.6784	0.6875
0.261	41.0	82	0.6631	0.7188
0.2945	42.0	84	0.6438	0.75
0.2734	43.0	86	0.7086	0.75
0.2536	44.0	88	0.6380	0.7188
0.2643	45.0	90	0.6723	0.6562
0.2273	46.0	92	0.6775	0.7188
0.235	47.0	94	0.6876	0.7188
0.2642	48.0	96	0.6382	0.7188
0.2467	49.0	98	0.6701	0.7188
0.2382	50.0	100	0.5663	0.7812

Framework versions

• Transformers 4.46.3
• Pytorch 2.4.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3