detr_finetuned_cppe5

NotSarahConnor1984/detr_finetuned_cppe5

detr_finetuned_cppe5 is a object detection model from NotSarahConnor1984. Use it when you need objects located in an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Object Detection
Library: transformers
License: apache-2.0
Model type: conditional_detr
Access: Public
Created: May 29, 2024
Updated: May 30, 2024
SHA: 9e9efaa5

Base models

microsoft/conditional-detr-resnet-50

Model card

From the Hugging Face model README

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: 1.2426
Map: 0.268
Map 50: 0.5294
Map 75: 0.2419
Map Small: 0.1163
Map Medium: 0.2288
Map Large: 0.5006
Mar 1: 0.2865
Mar 10: 0.4475
Mar 100: 0.4749
Mar Small: 0.3002
Mar Medium: 0.4623
Mar Large: 0.7345
Map Coverall: 0.5546
Mar 100 Coverall: 0.6736
Map Face Shield: 0.1674
Mar 100 Face Shield: 0.4833
Map Gloves: 0.1944
Mar 100 Gloves: 0.3662
Map Goggles: 0.1199
Mar 100 Goggles: 0.4421
Map Mask: 0.3036
Mar 100 Mask: 0.4092

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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 Coverall	Mar 100 Coverall	Map Face Shield	Mar 100 Face Shield	Map Gloves	Mar 100 Gloves	Map Goggles	Mar 100 Goggles	Map Mask	Mar 100 Mask
No log	1.0	102	2.3847	0.008	0.0259	0.0031	0.0071	0.0113	0.0323	0.0303	0.1094	0.1661	0.1002	0.1711	0.2087	0.0105	0.207	0.0034	0.0681	0.0078	0.2167	0.0025	0.0807	0.0156	0.2583
No log	2.0	204	2.1346	0.0292	0.0819	0.0147	0.0126	0.0268	0.042	0.0773	0.1631	0.2064	0.0815	0.1912	0.2543	0.0873	0.3791	0.0083	0.1153	0.0116	0.2069	0.0	0.0	0.0388	0.3306
No log	3.0	306	2.0183	0.0546	0.1289	0.0413	0.0288	0.0352	0.0606	0.1234	0.2329	0.2765	0.1078	0.2436	0.3393	0.1671	0.5567	0.0187	0.2167	0.0094	0.2157	0.0346	0.0912	0.0431	0.3024
No log	4.0	408	1.9305	0.0779	0.1742	0.0587	0.02	0.0476	0.1114	0.1417	0.2692	0.3059	0.0926	0.2783	0.4861	0.27	0.59	0.0335	0.3208	0.0214	0.2377	0.0036	0.086	0.061	0.2951
3.3072	5.0	510	1.7155	0.1186	0.2742	0.0935	0.0303	0.0871	0.2195	0.1434	0.3145	0.3401	0.1573	0.3076	0.5985	0.3762	0.5582	0.0213	0.2569	0.0463	0.2892	0.0081	0.2333	0.1414	0.3626
3.3072	6.0	612	1.6430	0.1371	0.301	0.1037	0.0347	0.1046	0.2527	0.1733	0.3374	0.3664	0.1605	0.3393	0.5973	0.4248	0.601	0.0216	0.2875	0.0572	0.3059	0.0243	0.2877	0.1577	0.35
3.3072	7.0	714	1.5879	0.1537	0.345	0.1218	0.0534	0.1358	0.2768	0.1933	0.3528	0.3807	0.1858	0.3554	0.6497	0.4326	0.6124	0.034	0.3486	0.0719	0.2917	0.0246	0.2877	0.2056	0.3631
3.3072	8.0	816	1.5310	0.1649	0.3587	0.1398	0.0579	0.1333	0.3036	0.1925	0.3729	0.3946	0.1854	0.3742	0.6476	0.4674	0.6353	0.0415	0.3722	0.0933	0.301	0.0387	0.3333	0.1836	0.3311
3.3072	9.0	918	1.4758	0.1789	0.3922	0.1478	0.0668	0.1372	0.3167	0.2241	0.3745	0.405	0.2063	0.3752	0.6691	0.4539	0.6199	0.063	0.4139	0.1041	0.3108	0.0375	0.3053	0.236	0.3752
1.4864	10.0	1020	1.4622	0.1735	0.3827	0.1333	0.05	0.1411	0.354	0.2103	0.371	0.3951	0.183	0.3664	0.6752	0.4784	0.6313	0.053	0.3903	0.1182	0.3186	0.0195	0.3053	0.1985	0.3301
1.4864	11.0	1122	1.4252	0.1858	0.4134	0.1496	0.0591	0.1632	0.3561	0.2227	0.3873	0.4144	0.1911	0.4137	0.6645	0.4794	0.6488	0.0752	0.4153	0.1131	0.3034	0.0292	0.3439	0.2319	0.3607
1.4864	12.0	1224	1.3893	0.1973	0.4218	0.1643	0.0749	0.169	0.4139	0.242	0.4054	0.4302	0.2226	0.4175	0.6991	0.4854	0.6413	0.0662	0.4292	0.1319	0.3397	0.0503	0.3561	0.2529	0.3845
1.4864	13.0	1326	1.3891	0.1998	0.431	0.1596	0.0675	0.1829	0.3762	0.2277	0.3962	0.4222	0.1979	0.4311	0.7011	0.504	0.6428	0.0911	0.4333	0.1384	0.3353	0.0552	0.3702	0.2101	0.3296
1.4864	14.0	1428	1.3981	0.193	0.42	0.1614	0.0698	0.1693	0.3523	0.235	0.3978	0.4271	0.2379	0.42	0.6729	0.4962	0.6557	0.0681	0.4278	0.136	0.3451	0.0493	0.3298	0.2155	0.3772
1.2306	15.0	1530	1.3472	0.217	0.4617	0.1785	0.0857	0.1817	0.4264	0.2416	0.4046	0.4329	0.2377	0.4143	0.7007	0.5137	0.6363	0.0968	0.4611	0.1571	0.3475	0.0484	0.3509	0.2689	0.3684
1.2306	16.0	1632	1.3450	0.227	0.4747	0.1915	0.0861	0.1891	0.4373	0.2521	0.4104	0.439	0.2503	0.4112	0.7344	0.5183	0.6428	0.1179	0.4514	0.1589	0.3289	0.0684	0.3912	0.2717	0.3806
1.2306	17.0	1734	1.2998	0.2359	0.4833	0.202	0.1089	0.1972	0.4426	0.2661	0.4303	0.4475	0.2792	0.4221	0.6999	0.5251	0.6463	0.12	0.4556	0.1646	0.3466	0.0857	0.393	0.284	0.3961
1.2306	18.0	1836	1.2995	0.2376	0.4866	0.1989	0.0926	0.2056	0.4487	0.2711	0.4325	0.4575	0.2798	0.4319	0.7195	0.522	0.6542	0.1299	0.475	0.1636	0.3544	0.0838	0.4018	0.2884	0.4019
1.2306	19.0	1938	1.2998	0.2362	0.4948	0.1954	0.1036	0.1905	0.4647	0.2563	0.4277	0.4446	0.249	0.4216	0.7165	0.5308	0.6672	0.1334	0.4722	0.1829	0.3407	0.0721	0.3772	0.2617	0.3655
1.0733	20.0	2040	1.2773	0.2513	0.5082	0.2298	0.1057	0.2148	0.4873	0.2723	0.4393	0.4678	0.2749	0.4514	0.7437	0.5342	0.6652	0.1499	0.4556	0.1754	0.3534	0.1101	0.4561	0.287	0.4087
1.0733	21.0	2142	1.2668	0.2516	0.5077	0.2323	0.1048	0.2104	0.4929	0.2758	0.4353	0.4592	0.2787	0.4287	0.7393	0.541	0.6692	0.1386	0.4653	0.1778	0.3525	0.1074	0.4018	0.2933	0.4073
1.0733	22.0	2244	1.2665	0.2496	0.5166	0.2143	0.114	0.2045	0.4759	0.2609	0.4314	0.454	0.2708	0.4246	0.7292	0.5355	0.6577	0.1393	0.4556	0.182	0.3657	0.1069	0.4	0.2842	0.3913
1.0733	23.0	2346	1.2512	0.2585	0.5258	0.2298	0.1196	0.2121	0.4884	0.2789	0.4465	0.4695	0.2991	0.4453	0.7262	0.5455	0.6672	0.1491	0.4833	0.1899	0.373	0.1149	0.4211	0.2931	0.4029
1.0733	24.0	2448	1.2511	0.2639	0.5275	0.2388	0.1198	0.2218	0.511	0.2845	0.4464	0.47	0.2911	0.4511	0.7377	0.5482	0.6657	0.1549	0.4694	0.192	0.3725	0.125	0.4386	0.2994	0.4039
0.9823	25.0	2550	1.2495	0.2629	0.5392	0.2309	0.1173	0.2213	0.4926	0.2828	0.4429	0.467	0.2888	0.4478	0.7363	0.549	0.6672	0.1633	0.4792	0.1931	0.3652	0.1181	0.4263	0.2908	0.3971
0.9823	26.0	2652	1.2470	0.2653	0.5276	0.2364	0.1136	0.2258	0.5082	0.2884	0.4486	0.4715	0.3017	0.4567	0.7313	0.5535	0.6701	0.1641	0.475	0.192	0.3672	0.1162	0.4368	0.3007	0.4083
0.9823	27.0	2754	1.2471	0.2661	0.5287	0.2366	0.1138	0.227	0.5013	0.2809	0.4483	0.4736	0.2986	0.4636	0.7286	0.5519	0.6711	0.1687	0.4806	0.1934	0.3676	0.1135	0.4404	0.3031	0.4083
0.9823	28.0	2856	1.2434	0.2673	0.5291	0.242	0.1156	0.229	0.5028	0.2866	0.4462	0.4745	0.3008	0.461	0.7367	0.5555	0.6736	0.1651	0.4806	0.1951	0.3662	0.1179	0.4421	0.3028	0.4102
0.9823	29.0	2958	1.2427	0.2676	0.5272	0.2425	0.116	0.2286	0.5	0.2863	0.4472	0.4745	0.299	0.4623	0.7343	0.554	0.6721	0.1675	0.4833	0.1942	0.3667	0.1195	0.4404	0.3027	0.4102
0.9316	30.0	3060	1.2426	0.268	0.5294	0.2419	0.1163	0.2288	0.5006	0.2865	0.4475	0.4749	0.3002	0.4623	0.7345	0.5546	0.6736	0.1674	0.4833	0.1944	0.3662	0.1199	0.4421	0.3036	0.4092

Framework versions

Transformers 4.41.1
Pytorch 2.2.2+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Task: Object Detection
Library: transformers
Type: conditional_detr
License: apache-2.0
Created: May 29, 2024
Updated: May 30, 2024