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hxwk507/
detr_finetuned_cppe5

Name: detr_finetuned_cppe5
Availability: InStock

generated_from_trainer

microsoft/conditional-detr-resnet-50

finetune

apache-2.0

endpoints_compatible

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detr_finetuned_cppe5

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8041
Map: 0.4041
Map 50: 0.8246
Map 75: 0.3402
Map Small: 0.3079
Map Medium: 0.3562
Map Large: 0.6364
Mar 1: 0.1839
Mar 10: 0.4794
Mar 100: 0.5657
Mar Small: 0.4329
Mar Medium: 0.5174
Mar Large: 0.7856
Map Hardhat: 0.4075
Mar 100 Hardhat: 0.5473
Map No-hardhat: 0.4007
Mar 100 No-hardhat: 0.5842

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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
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 Hardhat	Mar 100 Hardhat	Map No-hardhat	Mar 100 No-hardhat
No log	1.0	125	1.1847	0.0822	0.1954	0.0553	0.0796	0.0937	0.2324	0.1312	0.3509	0.4354	0.2086	0.3775	0.6788	0.1426	0.5655	0.0218	0.3053
No log	2.0	250	1.0931	0.1205	0.2648	0.0965	0.1277	0.1601	0.1647	0.1587	0.3817	0.4625	0.2886	0.435	0.6636	0.1868	0.5618	0.0542	0.3632
No log	3.0	375	1.1882	0.1449	0.3805	0.0834	0.0775	0.1623	0.3615	0.1042	0.3262	0.4281	0.3086	0.347	0.5962	0.2321	0.5036	0.0577	0.3526
1.472	4.0	500	1.0418	0.2419	0.6022	0.1416	0.1374	0.3265	0.3723	0.1444	0.4138	0.4827	0.2986	0.5035	0.6114	0.2844	0.5127	0.1994	0.4526
1.472	5.0	625	1.0232	0.2349	0.5815	0.1564	0.1736	0.3006	0.3826	0.1395	0.3992	0.4974	0.2686	0.5149	0.6689	0.2891	0.5527	0.1807	0.4421
1.472	6.0	750	0.9985	0.293	0.6561	0.2108	0.2129	0.3184	0.426	0.1604	0.4373	0.5125	0.3129	0.5225	0.6879	0.3452	0.5618	0.2407	0.4632
1.472	7.0	875	0.9616	0.3145	0.7258	0.2615	0.2491	0.3435	0.4999	0.1381	0.4634	0.5455	0.3171	0.5568	0.7386	0.3164	0.5436	0.3126	0.5474
0.9939	8.0	1000	0.9688	0.3194	0.7461	0.1786	0.1922	0.3171	0.576	0.16	0.4325	0.5252	0.2943	0.5194	0.7462	0.2982	0.4873	0.3405	0.5632
0.9939	9.0	1125	0.9211	0.3572	0.7888	0.2944	0.2046	0.3196	0.5454	0.1482	0.4632	0.5346	0.2571	0.5625	0.7303	0.3545	0.5218	0.3599	0.5474
0.9939	10.0	1250	0.9664	0.3463	0.7569	0.2541	0.2503	0.3473	0.4924	0.1611	0.4344	0.5116	0.31	0.499	0.7258	0.3423	0.5127	0.3504	0.5105
0.9939	11.0	1375	0.9463	0.3261	0.8263	0.2012	0.2582	0.2676	0.5931	0.1643	0.4153	0.5134	0.32	0.4437	0.7629	0.3386	0.5164	0.3136	0.5105
0.8775	12.0	1500	0.9153	0.3571	0.7972	0.2882	0.2397	0.3273	0.5803	0.1587	0.4377	0.5556	0.3571	0.566	0.7189	0.3347	0.5164	0.3795	0.5947
0.8775	13.0	1625	0.9063	0.3512	0.8299	0.2471	0.2342	0.3119	0.6117	0.1695	0.4222	0.5016	0.2786	0.4672	0.7439	0.3422	0.4927	0.3602	0.5105
0.8775	14.0	1750	0.9384	0.3351	0.7633	0.2393	0.1938	0.3064	0.5551	0.1723	0.4257	0.5105	0.3371	0.4718	0.7076	0.3496	0.5	0.3206	0.5211
0.8775	15.0	1875	0.8734	0.3836	0.8279	0.3055	0.2541	0.348	0.614	0.1748	0.4373	0.531	0.3729	0.4941	0.7386	0.3671	0.5145	0.4002	0.5474
0.7888	16.0	2000	0.8470	0.3763	0.8437	0.2603	0.2854	0.3289	0.5821	0.1822	0.4556	0.5455	0.4314	0.4861	0.7568	0.3894	0.5436	0.3633	0.5474
0.7888	17.0	2125	0.8579	0.3708	0.8189	0.2792	0.2701	0.2976	0.6115	0.185	0.443	0.5206	0.3957	0.4633	0.7326	0.3986	0.5255	0.343	0.5158
0.7888	18.0	2250	0.8404	0.3714	0.7962	0.2522	0.2531	0.3327	0.6139	0.1778	0.4587	0.5433	0.3729	0.5084	0.7455	0.3709	0.5182	0.3719	0.5684
0.7888	19.0	2375	0.8268	0.3997	0.8285	0.3108	0.2915	0.3526	0.5942	0.1829	0.4882	0.5481	0.4157	0.4986	0.7644	0.4014	0.5436	0.3979	0.5526
0.7048	20.0	2500	0.8091	0.4209	0.8122	0.4316	0.2668	0.377	0.6569	0.1964	0.4669	0.5568	0.4	0.5206	0.7462	0.4154	0.54	0.4265	0.5737
0.7048	21.0	2625	0.8206	0.416	0.8227	0.303	0.3221	0.3747	0.6208	0.1839	0.4811	0.5401	0.3729	0.4992	0.747	0.4198	0.5382	0.4121	0.5421
0.7048	22.0	2750	0.8108	0.4266	0.8502	0.4021	0.3038	0.3847	0.6317	0.1965	0.4688	0.5534	0.4257	0.5125	0.7515	0.4264	0.5436	0.4269	0.5632
0.7048	23.0	2875	0.8239	0.4103	0.8158	0.3492	0.2874	0.3626	0.6316	0.1919	0.4572	0.5533	0.4114	0.5152	0.7462	0.417	0.5382	0.4036	0.5684
0.6439	24.0	3000	0.8092	0.4077	0.825	0.3504	0.3205	0.3525	0.6074	0.1893	0.4883	0.5641	0.4357	0.5228	0.7652	0.4129	0.5545	0.4026	0.5737
0.6439	25.0	3125	0.8076	0.4104	0.8432	0.3547	0.316	0.3559	0.6302	0.1893	0.4689	0.5535	0.4429	0.5027	0.7515	0.4187	0.5491	0.4021	0.5579
0.6439	26.0	3250	0.7988	0.4133	0.837	0.3469	0.3285	0.3631	0.6222	0.2035	0.4849	0.573	0.4643	0.5166	0.7902	0.4219	0.5618	0.4048	0.5842
0.6439	27.0	3375	0.8015	0.4082	0.832	0.3262	0.3104	0.3619	0.62	0.1699	0.4785	0.5693	0.4429	0.5174	0.7902	0.4165	0.5491	0.3998	0.5895
0.6079	28.0	3500	0.8043	0.4064	0.824	0.3412	0.3052	0.3588	0.6301	0.1857	0.4785	0.5631	0.4329	0.5111	0.7856	0.4145	0.5473	0.3982	0.5789
0.6079	29.0	3625	0.8043	0.403	0.8246	0.3378	0.3082	0.3533	0.6358	0.1839	0.4794	0.5667	0.44	0.5174	0.7856	0.4076	0.5491	0.3984	0.5842
0.6079	30.0	3750	0.8041	0.4041	0.8246	0.3402	0.3079	0.3562	0.6364	0.1839	0.4794	0.5657	0.4329	0.5174	0.7856	0.4075	0.5473	0.4007	0.5842

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

hxwk507/detr_finetuned_cppe5

detr_finetuned_cppe5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hxwk507/
detr_finetuned_cppe5