rfdetr-docvqa-media3-trainval-agree1-medium

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

Loss: 17.1433
Map: 0.2703
Map 50: 0.4858
Map 75: 0.2504
Map Small: -1.0
Map Medium: 0.0548
Map Large: 0.2829
Mar 1: 0.3017
Mar 10: 0.5088
Mar 100: 0.5802
Mar Small: -1.0
Mar Medium: 0.0818
Mar Large: 0.6086
Map Chart: 0.2876
Mar 100 Chart: 0.67
Map Image: 0.3634
Mar 100 Image: 0.6505
Map Signature: 0.1597
Mar 100 Signature: 0.42

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: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Chart	Mar 100 Chart	Map Image	Mar 100 Image	Map Signature	Mar 100 Signature
53.1977	1.0	42	28.6751	0.0006	0.0023	0.0	-1.0	0.0	0.0006	0.0017	0.0242	0.0754	-1.0	0.0	0.0776	0.0015	0.1425	0.0002	0.0838	0.0	0.0
18.3829	2.0	84	22.3031	0.0001	0.0003	0.0	-1.0	0.0	0.0001	0.0003	0.0111	0.0505	-1.0	0.0	0.0543	0.0	0.0	0.0003	0.1515	0.0	0.0
12.6798	3.0	126	18.0869	0.0013	0.0048	0.0006	-1.0	0.0001	0.0014	0.0116	0.0877	0.2137	-1.0	0.0429	0.224	0.0018	0.1675	0.0019	0.4525	0.0002	0.0211
12.3407	4.0	168	16.4894	0.023	0.0532	0.0209	-1.0	0.0028	0.0253	0.0933	0.2201	0.3409	-1.0	0.1701	0.3613	0.0072	0.0375	0.0465	0.6374	0.0153	0.3478
11.5202	5.0	210	16.6346	0.0759	0.1353	0.072	-1.0	0.0238	0.0804	0.1753	0.3318	0.4685	-1.0	0.2994	0.4833	0.0221	0.415	0.1495	0.6293	0.056	0.3611
10.8266	6.0	252	16.7964	0.0478	0.1013	0.0416	-1.0	0.0747	0.0492	0.1604	0.3358	0.5535	-1.0	0.2266	0.5731	0.0164	0.61	0.0912	0.6606	0.0358	0.39
10.5741	7.0	294	14.9717	0.0782	0.1557	0.0682	-1.0	0.0566	0.0819	0.2013	0.4125	0.5793	-1.0	0.2123	0.6014	0.0235	0.6475	0.1371	0.6626	0.0739	0.4278
10.4029	8.0	336	15.0839	0.0885	0.1835	0.0812	-1.0	0.0595	0.0932	0.2132	0.4473	0.5749	-1.0	0.1299	0.5994	0.0341	0.6925	0.161	0.6545	0.0704	0.3778
9.9259	9.0	378	14.9783	0.1416	0.2663	0.1187	-1.0	0.0359	0.1495	0.2359	0.4447	0.6108	-1.0	0.2318	0.6339	0.0716	0.69	0.2244	0.699	0.1288	0.4433
9.5176	10.0	420	15.0226	0.1601	0.302	0.1381	-1.0	0.1018	0.1691	0.2342	0.4814	0.6045	-1.0	0.3084	0.6235	0.0783	0.69	0.2668	0.6869	0.1352	0.4367
9.3748	11.0	462	16.2226	0.1894	0.3387	0.1733	-1.0	0.0955	0.1964	0.2599	0.4897	0.6145	-1.0	0.2682	0.6361	0.159	0.7025	0.2695	0.7111	0.1397	0.43
9.2042	12.0	504	15.7788	0.1941	0.3492	0.1943	-1.0	0.1027	0.2004	0.2632	0.5003	0.6026	-1.0	0.2773	0.6214	0.1436	0.7075	0.3131	0.7081	0.1255	0.3922
8.8070	13.0	546	15.3817	0.2003	0.3608	0.1933	-1.0	0.1393	0.2074	0.2804	0.5062	0.6149	-1.0	0.2708	0.6353	0.177	0.73	0.2941	0.7081	0.1297	0.4067
8.7009	14.0	588	16.3511	0.2263	0.4101	0.2175	-1.0	0.0849	0.2353	0.2889	0.5054	0.6006	-1.0	0.1864	0.6235	0.2408	0.715	0.288	0.6879	0.1502	0.3989
8.7887	15.0	630	15.6987	0.2323	0.4316	0.2118	-1.0	0.0478	0.2447	0.2927	0.4922	0.5853	-1.0	0.1922	0.6087	0.1911	0.6825	0.3247	0.6657	0.1812	0.4078
8.3357	16.0	672	16.1288	0.2687	0.4568	0.2566	-1.0	0.057	0.2795	0.2939	0.5004	0.5913	-1.0	0.2201	0.6122	0.311	0.7025	0.339	0.6859	0.1563	0.3856
8.1628	17.0	714	16.2324	0.2595	0.4474	0.2618	-1.0	0.0486	0.2733	0.2994	0.5051	0.6005	-1.0	0.0916	0.6281	0.2617	0.7375	0.3408	0.6596	0.1758	0.4044
7.9911	18.0	756	16.4372	0.2829	0.4742	0.2856	-1.0	0.0997	0.2936	0.3091	0.4983	0.5905	-1.0	0.2325	0.6124	0.3094	0.6675	0.3641	0.6939	0.1752	0.41
8.0354	19.0	798	16.2935	0.2817	0.4902	0.2885	-1.0	0.0872	0.2925	0.3144	0.5133	0.5951	-1.0	0.1903	0.6167	0.3241	0.705	0.3462	0.6869	0.1749	0.3933
7.6743	20.0	840	17.1765	0.2633	0.4703	0.2387	-1.0	0.054	0.2757	0.3124	0.506	0.5809	-1.0	0.0942	0.6073	0.3024	0.705	0.3233	0.6576	0.1643	0.38
7.5240	21.0	882	17.1978	0.2816	0.4809	0.2598	-1.0	0.0604	0.2951	0.2959	0.4943	0.5849	-1.0	0.0994	0.6134	0.2918	0.6725	0.3733	0.6667	0.1798	0.4156
7.3807	22.0	924	17.3134	0.2652	0.4629	0.2384	-1.0	0.0692	0.2771	0.2969	0.4931	0.5889	-1.0	0.1266	0.6138	0.2729	0.71	0.3514	0.6566	0.1714	0.4
7.1151	23.0	966	17.0971	0.2742	0.4702	0.2667	-1.0	0.0495	0.2865	0.3096	0.509	0.6008	-1.0	0.1045	0.6275	0.309	0.7175	0.3481	0.6616	0.1656	0.4233
6.9313	24.0	1008	17.1139	0.2742	0.4647	0.2899	-1.0	0.0612	0.2866	0.3096	0.5009	0.5832	-1.0	0.113	0.6094	0.3169	0.69	0.3259	0.6606	0.1799	0.3989
7.0055	25.0	1050	16.9771	0.2823	0.5006	0.2639	-1.0	0.0678	0.2947	0.3096	0.5099	0.5819	-1.0	0.1214	0.6074	0.2902	0.6775	0.3722	0.6505	0.1845	0.4178
6.8643	26.0	1092	17.1671	0.2717	0.4797	0.2636	-1.0	0.0522	0.2844	0.3102	0.5146	0.5999	-1.0	0.0981	0.628	0.292	0.705	0.3676	0.6626	0.1555	0.4322
6.7321	27.0	1134	17.1871	0.2832	0.4964	0.2763	-1.0	0.0493	0.2963	0.3067	0.4882	0.5683	-1.0	0.0682	0.5972	0.326	0.65	0.3708	0.6515	0.1528	0.4033
7.1077	28.0	1176	17.1420	0.2683	0.4745	0.2651	-1.0	0.0577	0.281	0.2997	0.5036	0.5741	-1.0	0.0818	0.6024	0.2823	0.6575	0.3686	0.6414	0.1539	0.4233
6.7518	29.0	1218	17.1374	0.276	0.4947	0.2527	-1.0	0.0548	0.2887	0.3107	0.5005	0.5766	-1.0	0.0818	0.6048	0.3023	0.6675	0.3673	0.6424	0.1585	0.42
6.9678	30.0	1260	17.1433	0.2703	0.4858	0.2504	-1.0	0.0548	0.2829	0.3017	0.5088	0.5802	-1.0	0.0818	0.6086	0.2876	0.67	0.3634	0.6505	0.1597	0.42

Framework versions

Transformers 5.12.1
Pytorch 2.12.0+cu130
Datasets 5.0.0
Tokenizers 0.22.2

merve/rfdetr-docvqa-media3-trainval-agree1-medium

rfdetr-docvqa-media3-trainval-agree1-medium

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model