ViTGPT2I2A

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

• Loss: 0.0708

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: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 4
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.1528	0.17	1000	0.0869
0.0899	0.34	2000	0.0817
0.084	0.51	3000	0.0790
0.0814	0.68	4000	0.0773
0.0803	0.85	5000	0.0757
0.077	1.02	6000	0.0745
0.0739	1.19	7000	0.0740
0.0719	1.37	8000	0.0737
0.0717	1.54	9000	0.0730
0.0731	1.71	10000	0.0727
0.0708	1.88	11000	0.0720
0.0697	2.05	12000	0.0717
0.0655	2.22	13000	0.0719
0.0653	2.39	14000	0.0719
0.0657	2.56	15000	0.0712
0.0663	2.73	16000	0.0710
0.0654	2.9	17000	0.0708
0.0645	3.07	18000	0.0716
0.0616	3.24	19000	0.0712
0.0607	3.41	20000	0.0712
0.0611	3.58	21000	0.0711
0.0615	3.76	22000	0.0711
0.0614	3.93	23000	0.0710
0.0594	4.1	24000	0.0716
0.0587	4.27	25000	0.0715
0.0574	4.44	26000	0.0715
0.0579	4.61	27000	0.0715
0.0581	4.78	28000	0.0715
0.0579	4.95	29000	0.0715

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.2+cu113
• Datasets 1.18.3
• Tokenizers 0.11.0