ViTGPT2_VW

gagan3012/ViTGPT2_VW

ViTGPT2_VW is a image-text-to-text model from gagan3012. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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Hugging Face

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At a glance

Task: Image-Text-to-Text
Library: transformers
Model type: vision-encoder-decoder
Access: Public
Created: Mar 2, 2022
Updated: Feb 7, 2022
SHA: 93c31484

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Model card

From the Hugging Face model README

ViTGPT2_VW

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0771

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: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.1256	0.03	1000	0.0928
0.0947	0.07	2000	0.0897
0.0889	0.1	3000	0.0859
0.0888	0.14	4000	0.0842
0.0866	0.17	5000	0.0831
0.0852	0.2	6000	0.0819
0.0833	0.24	7000	0.0810
0.0835	0.27	8000	0.0802
0.081	0.31	9000	0.0796
0.0803	0.34	10000	0.0789
0.0814	0.38	11000	0.0785
0.0799	0.41	12000	0.0780
0.0786	0.44	13000	0.0776
0.0796	0.48	14000	0.0771

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Mar 2, 2022
Updated: Feb 7, 2022