The WildReceipt dataset consists of 1740 receipt images, and contains 25 key information categories, and a total of about 69000 text boxes. 1268 and 472 images are used for training and testing respectively to train the LayoutLMv3 model for Key Information Extraction.

Training procedure

The training code: https://github.com/Theivaprakasham/layoutlmv3/blob/main/training_codes/LayoutLMv3_training_WildReceipts_dataset.ipynb

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.32	100	1.3143	0.6709	0.2679	0.3829	0.6700
No log	0.63	200	0.8814	0.6478	0.5195	0.5766	0.7786
No log	0.95	300	0.6568	0.7205	0.6491	0.6829	0.8303
No log	1.26	400	0.5618	0.7544	0.7072	0.7300	0.8519
1.0284	1.58	500	0.5003	0.7802	0.7566	0.7682	0.8687
1.0284	1.89	600	0.4454	0.7941	0.7679	0.7807	0.8748
1.0284	2.21	700	0.4314	0.8142	0.7928	0.8033	0.8852
1.0284	2.52	800	0.3870	0.8172	0.8200	0.8186	0.8953
1.0284	2.84	900	0.3629	0.8288	0.8369	0.8329	0.9025
0.4167	3.15	1000	0.3537	0.8540	0.8200	0.8366	0.9052
0.4167	3.47	1100	0.3383	0.8438	0.8285	0.8361	0.9063
0.4167	3.79	1200	0.3403	0.8297	0.8493	0.8394	0.9062
0.4167	4.1	1300	0.3271	0.8428	0.8545	0.8487	0.9110
0.4167	4.42	1400	0.3182	0.8491	0.8518	0.8504	0.9131
0.2766	4.73	1500	0.3111	0.8491	0.8539	0.8515	0.9129
0.2766	5.05	1600	0.3177	0.8397	0.8620	0.8507	0.9124
0.2766	5.36	1700	0.3091	0.8676	0.8548	0.8612	0.9191
0.2766	5.68	1800	0.3080	0.8508	0.8645	0.8576	0.9162
0.2766	5.99	1900	0.3059	0.8492	0.8662	0.8576	0.9163
0.2114	6.31	2000	0.3184	0.8536	0.8657	0.8596	0.9147
0.2114	6.62	2100	0.3161	0.8583	0.8713	0.8648	0.9184
0.2114	6.94	2200	0.3055	0.8707	0.8682	0.8694	0.9220
0.2114	7.26	2300	0.3004	0.8689	0.8745	0.8717	0.9219
0.2114	7.57	2400	0.3111	0.8701	0.8720	0.8711	0.9211
0.174	7.89	2500	0.3130	0.8599	0.8741	0.8669	0.9198
0.174	8.2	2600	0.3034	0.8661	0.8748	0.8704	0.9219
0.174	8.52	2700	0.3005	0.8799	0.8673	0.8736	0.9225
0.174	8.83	2800	0.3043	0.8687	0.8804	0.8745	0.9240
0.174	9.15	2900	0.3121	0.8776	0.8704	0.8740	0.9242
0.1412	9.46	3000	0.3131	0.8631	0.8755	0.8692	0.9204
0.1412	9.78	3100	0.3067	0.8715	0.8773	0.8744	0.9233
0.1412	10.09	3200	0.3021	0.8751	0.8812	0.8782	0.9248
0.1412	10.41	3300	0.3092	0.8651	0.8808	0.8729	0.9228
0.1412	10.73	3400	0.3084	0.8776	0.8749	0.8762	0.9237
0.1254	11.04	3500	0.3156	0.8738	0.8785	0.8761	0.9237
0.1254	11.36	3600	0.3131	0.8723	0.8818	0.8770	0.9244
0.1254	11.67	3700	0.3108	0.8778	0.8781	0.8780	0.9250
0.1254	11.99	3800	0.3097	0.8778	0.8771	0.8775	0.9239
0.1254	12.3	3900	0.3115	0.8785	0.8801	0.8793	0.9251
0.111	12.62	4000	0.3108	0.8772	0.8799	0.8785	0.9249

Framework versions

Transformers 4.20.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

Theivaprakasham/layoutlmv3-finetuned-wildreceipt

layoutlmv3-finetuned-wildreceipt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Theivaprakasham/
layoutlmv3-finetuned-wildreceipt