rfdetr-docvqa-qwen

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

Model description

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The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
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: 10

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 Table	Mar 100 Table
24.7458	1.0	69	21.2345	0.0008	0.0029	0.0001	-1.0	0.0	0.0008	0.0005	0.0168	0.0686	-1.0	0.0	0.0686	0.0001	0.0824	0.0	0.0	0.004	0.2605
13.3342	2.0	138	18.1056	0.0011	0.0033	0.0005	-1.0	0.0	0.0011	0.0063	0.0302	0.0661	-1.0	0.0	0.0662	0.0	0.0	0.0001	0.0085	0.0055	0.3221
12.3492	3.0	207	17.8376	0.0023	0.007	0.0008	-1.0	0.0	0.0023	0.0105	0.0414	0.1012	-1.0	0.0	0.103	0.0	0.0	0.0011	0.1712	0.0102	0.3349
11.0428	4.0	276	17.4588	0.0072	0.0227	0.0017	-1.0	0.0	0.0072	0.0154	0.0484	0.14	-1.0	0.0	0.1434	0.0	0.0	0.0058	0.322	0.03	0.3779
10.6341	5.0	345	15.4127	0.006	0.0164	0.0039	-1.0	0.0	0.0062	0.0115	0.0753	0.2016	-1.0	0.0	0.2077	0.0	0.0	0.0149	0.5695	0.0154	0.4384
10.1076	6.0	414	14.4452	0.0158	0.0308	0.0141	-1.0	0.0	0.0162	0.0307	0.1094	0.2111	-1.0	0.0	0.2175	0.0	0.0	0.0337	0.5983	0.0452	0.457
9.5315	7.0	483	13.9064	0.0165	0.0368	0.0108	-1.0	0.0	0.0171	0.024	0.1238	0.2374	-1.0	0.0	0.2445	0.0	0.0	0.0553	0.6661	0.0273	0.5209
8.9901	8.0	552	13.0197	0.0342	0.0613	0.0326	-1.0	0.0087	0.0355	0.0424	0.1634	0.2603	-1.0	0.1	0.266	0.0	0.0	0.1215	0.7305	0.0496	0.5709
9.2235	9.0	621	13.0330	0.0372	0.0558	0.0442	-1.0	0.0308	0.0389	0.0471	0.1659	0.2797	-1.0	0.35	0.2807	0.0	0.0	0.1592	0.7881	0.0269	0.6105
8.9338	10.0	690	13.0586	0.0368	0.053	0.043	-1.0	0.0361	0.0382	0.0457	0.1721	0.2857	-1.0	0.35	0.2867	0.0	0.0	0.1529	0.7949	0.031	0.6337