rtdetrv2-floorplan-v2

This model is a fine-tuned version of PekingU/rtdetr_v2_r50vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 6.2442
Map: 0.6663
Map 50: 0.758
Map 75: 0.7005
Map Small: 0.552
Map Medium: 0.8059
Map Large: 0.4146
Mar 1: 0.274
Mar 10: 0.8655
Mar 100: 0.9038
Mar Small: 0.7441
Mar Medium: 0.9257
Mar Large: 0.9488
Map Window: 0.7714
Mar 100 Window: 0.8784
Map Door: 0.685
Mar 100 Door: 0.9447
Map Stair: 0.5424
Mar 100 Stair: 0.8882

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: 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: 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 Window	Mar 100 Window	Map Door	Mar 100 Door	Map Stair	Mar 100 Stair
13.6845	1.0	578	8.5528	0.5313	0.6844	0.5936	0.2287	0.5989	0.5458	0.2374	0.7215	0.7936	0.463	0.8541	0.7595	0.3682	0.7491	0.7475	0.8681	0.4782	0.7635
10.3385	2.0	1156	6.6908	0.5567	0.7148	0.6232	0.3899	0.6625	0.4741	0.2622	0.78	0.8265	0.5912	0.8732	0.8967	0.5611	0.768	0.6255	0.907	0.4834	0.8045
10.2151	3.0	1734	6.5601	0.5095	0.6472	0.567	0.3784	0.7081	0.4322	0.2631	0.802	0.8571	0.6519	0.9027	0.9034	0.5552	0.8032	0.6693	0.9175	0.3041	0.8507
9.2546	4.0	2312	6.2673	0.5949	0.7158	0.6618	0.404	0.7555	0.3886	0.2769	0.828	0.8686	0.6662	0.9078	0.9106	0.5946	0.8324	0.7551	0.9233	0.4351	0.8502
9.3453	5.0	2890	6.3085	0.6816	0.827	0.7517	0.4526	0.769	0.5305	0.282	0.8251	0.8727	0.6768	0.9113	0.9165	0.6705	0.8387	0.7792	0.9244	0.5951	0.855
8.4808	6.0	3468	5.9946	0.6344	0.7475	0.6899	0.4436	0.8077	0.502	0.2834	0.8366	0.8734	0.6808	0.9095	0.9277	0.6886	0.8405	0.7785	0.929	0.436	0.8505
9.0634	7.0	4046	6.1850	0.6574	0.7845	0.7219	0.4659	0.7887	0.4464	0.2847	0.8309	0.8664	0.682	0.8975	0.921	0.7034	0.8185	0.8197	0.9343	0.4491	0.8465
8.0622	8.0	4624	6.1631	0.6279	0.7624	0.6877	0.4289	0.7938	0.532	0.2853	0.8349	0.8749	0.6876	0.9137	0.9219	0.6222	0.8297	0.777	0.935	0.4846	0.86
7.6981	9.0	5202	6.0414	0.6407	0.7597	0.7031	0.4487	0.8103	0.4129	0.287	0.839	0.8853	0.7143	0.9198	0.9257	0.6327	0.845	0.7607	0.9359	0.5288	0.8751
8.3466	10.0	5780	6.1942	0.6848	0.8023	0.7421	0.4724	0.8127	0.5437	0.2883	0.8494	0.8895	0.7206	0.9219	0.936	0.6855	0.8541	0.8042	0.9382	0.5647	0.8761
7.5818	11.0	6358	5.9822	0.6707	0.772	0.7174	0.47	0.8111	0.4432	0.2881	0.8557	0.8907	0.7052	0.9259	0.9377	0.6817	0.859	0.7765	0.9424	0.5537	0.8706
7.2809	12.0	6936	6.1977	0.6297	0.7388	0.6821	0.5046	0.7594	0.3041	0.2698	0.8467	0.8892	0.7288	0.918	0.9238	0.6957	0.8572	0.6876	0.9427	0.5058	0.8678
7.6799	13.0	7514	6.1385	0.6041	0.7042	0.6452	0.4528	0.8024	0.2979	0.286	0.8481	0.8891	0.7027	0.9146	0.9471	0.5694	0.8608	0.7867	0.9401	0.4562	0.8663
7.2585	14.0	8092	6.1522	0.6609	0.7527	0.7107	0.5289	0.8068	0.4284	0.2889	0.8572	0.903	0.7482	0.9261	0.9359	0.7054	0.8739	0.727	0.943	0.5503	0.8922
7.0676	15.0	8670	6.2892	0.5739	0.6646	0.6126	0.5032	0.7629	0.2753	0.2741	0.848	0.8936	0.7282	0.9257	0.9307	0.6037	0.8608	0.6299	0.9432	0.4881	0.8766
7.1380	16.0	9248	6.1258	0.6293	0.7269	0.6734	0.5023	0.8094	0.4487	0.282	0.8548	0.8887	0.7237	0.9138	0.9288	0.7037	0.8527	0.6997	0.9427	0.4844	0.8706
7.1766	17.0	9826	6.1451	0.6694	0.769	0.7187	0.5442	0.8052	0.457	0.2847	0.8608	0.8984	0.7421	0.9267	0.9511	0.7256	0.8604	0.7286	0.9462	0.5541	0.8888
7.1360	18.0	10404	6.1668	0.6648	0.7519	0.712	0.566	0.7945	0.4634	0.2823	0.8622	0.9019	0.756	0.9291	0.9461	0.7593	0.8676	0.6829	0.9479	0.5523	0.8901
6.9559	19.0	10982	6.1588	0.6791	0.772	0.7246	0.557	0.8104	0.4372	0.2824	0.8611	0.8994	0.7424	0.9275	0.9527	0.759	0.864	0.7181	0.9462	0.5602	0.8881
7.1326	20.0	11560	6.1483	0.7176	0.8173	0.7628	0.5658	0.8179	0.4932	0.2821	0.8606	0.901	0.7299	0.9275	0.9474	0.7704	0.8707	0.7394	0.9471	0.6431	0.8853
7.0184	21.0	12138	6.2647	0.6733	0.7712	0.7192	0.5379	0.7962	0.4218	0.2772	0.8578	0.8983	0.7219	0.9292	0.9466	0.763	0.8685	0.6811	0.9424	0.5759	0.8841
6.6626	22.0	12716	6.1979	0.6668	0.7613	0.7041	0.531	0.8107	0.4587	0.2815	0.8613	0.8983	0.7323	0.9224	0.9371	0.7483	0.8653	0.6948	0.9437	0.5574	0.8858
6.7067	23.0	13294	6.1718	0.6794	0.7699	0.7209	0.5409	0.8136	0.459	0.2849	0.8658	0.9034	0.7467	0.9268	0.9445	0.7598	0.8752	0.7114	0.9444	0.5669	0.8905
6.4054	24.0	13872	6.2339	0.685	0.7732	0.7252	0.5606	0.8116	0.449	0.2848	0.8644	0.9013	0.7418	0.9256	0.9476	0.7725	0.8689	0.6926	0.9449	0.59	0.89
6.6368	25.0	14450	6.2354	0.6627	0.7539	0.6948	0.5542	0.8049	0.4134	0.2795	0.8661	0.9047	0.7434	0.9259	0.9464	0.7693	0.8788	0.6685	0.9432	0.5504	0.8922
6.4800	26.0	15028	6.2556	0.6755	0.7654	0.7069	0.5524	0.8075	0.4269	0.2829	0.865	0.9048	0.7387	0.9277	0.9464	0.7774	0.8784	0.6718	0.9447	0.5772	0.8913
6.2285	27.0	15606	6.2242	0.6806	0.7728	0.7109	0.5542	0.8111	0.3867	0.2777	0.8657	0.904	0.7425	0.9263	0.9472	0.7778	0.8779	0.702	0.9456	0.5621	0.8884
6.4170	28.0	16184	6.2169	0.6885	0.7807	0.7222	0.5544	0.8102	0.4346	0.2843	0.8687	0.9058	0.7461	0.9287	0.9468	0.7788	0.8802	0.7048	0.9456	0.5818	0.8917
6.4861	29.0	16762	6.2537	0.6699	0.7618	0.7051	0.552	0.8008	0.4108	0.2804	0.8642	0.9042	0.7428	0.9256	0.9481	0.7717	0.8761	0.6867	0.9453	0.5514	0.8912
6.2960	30.0	17340	6.2442	0.6663	0.758	0.7005	0.552	0.8059	0.4146	0.274	0.8655	0.9038	0.7441	0.9257	0.9488	0.7714	0.8784	0.685	0.9447	0.5424	0.8882

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

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rtdetrv2-floorplan-v2

This model is a fine-tuned version of PekingU/rtdetr_v2_r50vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 6.2442

Map: 0.6663

Map 50: 0.758

Map 75: 0.7005

Map Small: 0.552

Map Medium: 0.8059

Map Large: 0.4146

Mar 1: 0.274

Mar 10: 0.8655

Mar 100: 0.9038

Mar Small: 0.7441

Mar Medium: 0.9257

Mar Large: 0.9488

Map Window: 0.7714

Mar 100 Window: 0.8784

Map Door: 0.685

Mar 100 Door: 0.9447

Map Stair: 0.5424

Mar 100 Stair: 0.8882

Training procedure

Training hyperparameters

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: 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 Window	Mar 100 Window	Map Door	Mar 100 Door	Map Stair	Mar 100 Stair
13.6845	1.0	578	8.5528	0.5313	0.6844	0.5936	0.2287	0.5989	0.5458	0.2374	0.7215	0.7936	0.463	0.8541	0.7595	0.3682	0.7491	0.7475	0.8681	0.4782	0.7635
10.3385	2.0	1156	6.6908	0.5567	0.7148	0.6232	0.3899	0.6625	0.4741	0.2622	0.78	0.8265	0.5912	0.8732	0.8967	0.5611	0.768	0.6255	0.907	0.4834	0.8045
10.2151	3.0	1734	6.5601	0.5095	0.6472	0.567	0.3784	0.7081	0.4322	0.2631	0.802	0.8571	0.6519	0.9027	0.9034	0.5552	0.8032	0.6693	0.9175	0.3041	0.8507
9.2546	4.0	2312	6.2673	0.5949	0.7158	0.6618	0.404	0.7555	0.3886	0.2769	0.828	0.8686	0.6662	0.9078	0.9106	0.5946	0.8324	0.7551	0.9233	0.4351	0.8502
9.3453	5.0	2890	6.3085	0.6816	0.827	0.7517	0.4526	0.769	0.5305	0.282	0.8251	0.8727	0.6768	0.9113	0.9165	0.6705	0.8387	0.7792	0.9244	0.5951	0.855
8.4808	6.0	3468	5.9946	0.6344	0.7475	0.6899	0.4436	0.8077	0.502	0.2834	0.8366	0.8734	0.6808	0.9095	0.9277	0.6886	0.8405	0.7785	0.929	0.436	0.8505
9.0634	7.0	4046	6.1850	0.6574	0.7845	0.7219	0.4659	0.7887	0.4464	0.2847	0.8309	0.8664	0.682	0.8975	0.921	0.7034	0.8185	0.8197	0.9343	0.4491	0.8465
8.0622	8.0	4624	6.1631	0.6279	0.7624	0.6877	0.4289	0.7938	0.532	0.2853	0.8349	0.8749	0.6876	0.9137	0.9219	0.6222	0.8297	0.777	0.935	0.4846	0.86
7.6981	9.0	5202	6.0414	0.6407	0.7597	0.7031	0.4487	0.8103	0.4129	0.287	0.839	0.8853	0.7143	0.9198	0.9257	0.6327	0.845	0.7607	0.9359	0.5288	0.8751
8.3466	10.0	5780	6.1942	0.6848	0.8023	0.7421	0.4724	0.8127	0.5437	0.2883	0.8494	0.8895	0.7206	0.9219	0.936	0.6855	0.8541	0.8042	0.9382	0.5647	0.8761
7.5818	11.0	6358	5.9822	0.6707	0.772	0.7174	0.47	0.8111	0.4432	0.2881	0.8557	0.8907	0.7052	0.9259	0.9377	0.6817	0.859	0.7765	0.9424	0.5537	0.8706
7.2809	12.0	6936	6.1977	0.6297	0.7388	0.6821	0.5046	0.7594	0.3041	0.2698	0.8467	0.8892	0.7288	0.918	0.9238	0.6957	0.8572	0.6876	0.9427	0.5058	0.8678
7.6799	13.0	7514	6.1385	0.6041	0.7042	0.6452	0.4528	0.8024	0.2979	0.286	0.8481	0.8891	0.7027	0.9146	0.9471	0.5694	0.8608	0.7867	0.9401	0.4562	0.8663
7.2585	14.0	8092	6.1522	0.6609	0.7527	0.7107	0.5289	0.8068	0.4284	0.2889	0.8572	0.903	0.7482	0.9261	0.9359	0.7054	0.8739	0.727	0.943	0.5503	0.8922
7.0676	15.0	8670	6.2892	0.5739	0.6646	0.6126	0.5032	0.7629	0.2753	0.2741	0.848	0.8936	0.7282	0.9257	0.9307	0.6037	0.8608	0.6299	0.9432	0.4881	0.8766
7.1380	16.0	9248	6.1258	0.6293	0.7269	0.6734	0.5023	0.8094	0.4487	0.282	0.8548	0.8887	0.7237	0.9138	0.9288	0.7037	0.8527	0.6997	0.9427	0.4844	0.8706
7.1766	17.0	9826	6.1451	0.6694	0.769	0.7187	0.5442	0.8052	0.457	0.2847	0.8608	0.8984	0.7421	0.9267	0.9511	0.7256	0.8604	0.7286	0.9462	0.5541	0.8888
7.1360	18.0	10404	6.1668	0.6648	0.7519	0.712	0.566	0.7945	0.4634	0.2823	0.8622	0.9019	0.756	0.9291	0.9461	0.7593	0.8676	0.6829	0.9479	0.5523	0.8901
6.9559	19.0	10982	6.1588	0.6791	0.772	0.7246	0.557	0.8104	0.4372	0.2824	0.8611	0.8994	0.7424	0.9275	0.9527	0.759	0.864	0.7181	0.9462	0.5602	0.8881
7.1326	20.0	11560	6.1483	0.7176	0.8173	0.7628	0.5658	0.8179	0.4932	0.2821	0.8606	0.901	0.7299	0.9275	0.9474	0.7704	0.8707	0.7394	0.9471	0.6431	0.8853
7.0184	21.0	12138	6.2647	0.6733	0.7712	0.7192	0.5379	0.7962	0.4218	0.2772	0.8578	0.8983	0.7219	0.9292	0.9466	0.763	0.8685	0.6811	0.9424	0.5759	0.8841
6.6626	22.0	12716	6.1979	0.6668	0.7613	0.7041	0.531	0.8107	0.4587	0.2815	0.8613	0.8983	0.7323	0.9224	0.9371	0.7483	0.8653	0.6948	0.9437	0.5574	0.8858
6.7067	23.0	13294	6.1718	0.6794	0.7699	0.7209	0.5409	0.8136	0.459	0.2849	0.8658	0.9034	0.7467	0.9268	0.9445	0.7598	0.8752	0.7114	0.9444	0.5669	0.8905
6.4054	24.0	13872	6.2339	0.685	0.7732	0.7252	0.5606	0.8116	0.449	0.2848	0.8644	0.9013	0.7418	0.9256	0.9476	0.7725	0.8689	0.6926	0.9449	0.59	0.89
6.6368	25.0	14450	6.2354	0.6627	0.7539	0.6948	0.5542	0.8049	0.4134	0.2795	0.8661	0.9047	0.7434	0.9259	0.9464	0.7693	0.8788	0.6685	0.9432	0.5504	0.8922
6.4800	26.0	15028	6.2556	0.6755	0.7654	0.7069	0.5524	0.8075	0.4269	0.2829	0.865	0.9048	0.7387	0.9277	0.9464	0.7774	0.8784	0.6718	0.9447	0.5772	0.8913
6.2285	27.0	15606	6.2242	0.6806	0.7728	0.7109	0.5542	0.8111	0.3867	0.2777	0.8657	0.904	0.7425	0.9263	0.9472	0.7778	0.8779	0.702	0.9456	0.5621	0.8884
6.4170	28.0	16184	6.2169	0.6885	0.7807	0.7222	0.5544	0.8102	0.4346	0.2843	0.8687	0.9058	0.7461	0.9287	0.9468	0.7788	0.8802	0.7048	0.9456	0.5818	0.8917
6.4861	29.0	16762	6.2537	0.6699	0.7618	0.7051	0.552	0.8008	0.4108	0.2804	0.8642	0.9042	0.7428	0.9256	0.9481	0.7717	0.8761	0.6867	0.9453	0.5514	0.8912
6.2960	30.0	17340	6.2442	0.6663	0.758	0.7005	0.552	0.8059	0.4146	0.274	0.8655	0.9038	0.7441	0.9257	0.9488	0.7714	0.8784	0.685	0.9447	0.5424	0.8882

Framework versions

Transformers 5.12.1

Pytorch 2.12.1+cu130

Datasets 5.0.0

Tokenizers 0.22.2

Voix7/rtdetrv2-floorplan-v2

rtdetrv2-floorplan-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rtdetrv2-floorplan-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions