vit-base_rvl-cdip

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5535
• Accuracy: 0.897
• Brier Loss: 0.1768
• Nll: 1.0978
• F1 Micro: 0.897
• F1 Macro: 0.8972
• Ece: 0.0801
• Aurc: 0.0180

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
0.676	1.0	5000	0.6451	0.8230	0.2574	1.2627	0.8230	0.8237	0.0458	0.0425
0.4207	2.0	10000	0.4251	0.8766	0.1800	1.2821	0.8766	0.8779	0.0154	0.0218
0.3335	3.0	15000	0.3914	0.8861	0.1676	1.2589	0.8861	0.8858	0.0252	0.0192
0.2447	4.0	20000	0.3687	0.8934	0.1574	1.2243	0.8934	0.8937	0.0331	0.0164
0.1623	5.0	25000	0.3843	0.8976	0.1583	1.1553	0.8976	0.8973	0.0461	0.0159
0.1083	6.0	30000	0.4131	0.8964	0.1624	1.1514	0.8964	0.8967	0.0581	0.0163
0.0652	7.0	35000	0.4633	0.8966	0.1690	1.1300	0.8966	0.8967	0.0692	0.0169
0.0361	8.0	40000	0.5068	0.8976	0.1723	1.1161	0.8976	0.8976	0.0737	0.0175
0.0192	9.0	45000	0.5418	0.8982	0.1748	1.1015	0.8982	0.8983	0.0779	0.0179
0.0111	10.0	50000	0.5535	0.897	0.1768	1.0978	0.897	0.8972	0.0801	0.0180

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1.post200
• Datasets 2.9.0
• Tokenizers 0.13.2