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: 2.9593
• Map: 0.0044
• Map 50: 0.0137
• Map 75: 0.0023
• Map Small: 0.0022
• Map Medium: 0.0004
• Map Large: 0.0048
• Mar 1: 0.0129
• Mar 10: 0.0353
• Mar 100: 0.0591
• Mar Small: 0.0018
• Mar Medium: 0.0246
• Mar Large: 0.0575
• Map Coverall: 0.0207
• Mar 100 Coverall: 0.2338
• Map Face Shield: 0.0001
• Mar 100 Face Shield: 0.0038
• Map Gloves: 0.0002
• Mar 100 Gloves: 0.021
• Map Goggles: 0.0
• Mar 100 Goggles: 0.0
• Map Mask: 0.001
• Mar 100 Mask: 0.0369

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	107	3.4694	0.0001	0.0007	0.0	0.0	0.0001	0.0002	0.0018	0.0054	0.0086	0.0057	0.0035	0.0055	0.0004	0.0239	0.0	0.0	0.0	0.0022	0.0	0.0	0.0001	0.0169
No log	2.0	214	3.3011	0.0009	0.0029	0.0003	0.0009	0.0	0.0009	0.0022	0.0183	0.0288	0.0011	0.007	0.0292	0.0042	0.1275	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0164
No log	3.0	321	3.4689	0.0012	0.0045	0.0003	0.0	0.0	0.0013	0.0032	0.0169	0.0355	0.0	0.0	0.0406	0.0059	0.1775	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No log	4.0	428	3.2984	0.0018	0.0077	0.0005	0.0002	0.0001	0.0021	0.005	0.0216	0.0346	0.0002	0.0169	0.0316	0.009	0.1383	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0347
4.926	5.0	535	3.1808	0.0019	0.0071	0.0005	0.0002	0.0001	0.0021	0.0032	0.0229	0.0445	0.0002	0.0157	0.0431	0.0093	0.1883	0.0	0.0	0.0	0.0089	0.0	0.0	0.0001	0.0253
4.926	6.0	642	3.1296	0.002	0.007	0.0007	0.0005	0.0001	0.0022	0.0059	0.0207	0.0487	0.0015	0.0168	0.0477	0.0099	0.2095	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0342
4.926	7.0	749	3.1212	0.0021	0.007	0.0007	0.0007	0.0008	0.0024	0.0029	0.0255	0.0505	0.0007	0.0143	0.051	0.0104	0.2234	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0289
4.926	8.0	856	3.2044	0.0045	0.0148	0.0014	0.0007	0.0	0.0051	0.0095	0.0208	0.037	0.0007	0.0108	0.0363	0.0222	0.1586	0.0	0.0	0.0001	0.0129	0.0	0.0	0.0	0.0133
4.926	9.0	963	3.1113	0.0028	0.0104	0.0005	0.004	0.0001	0.0032	0.0111	0.0237	0.0436	0.0031	0.0133	0.0421	0.014	0.1838	0.0	0.0	0.0	0.0058	0.0	0.0	0.0002	0.0284
3.0252	10.0	1070	3.1235	0.0038	0.0142	0.0013	0.0013	0.0	0.0039	0.0039	0.0283	0.0506	0.0016	0.007	0.0534	0.0167	0.2333	0.0	0.0	0.0	0.0107	0.0	0.0	0.0021	0.0089
3.0252	11.0	1177	3.0521	0.0041	0.0136	0.0015	0.0062	0.0	0.0042	0.0121	0.0309	0.051	0.0062	0.0081	0.0514	0.0185	0.2248	0.0	0.0	0.0001	0.0071	0.0	0.0	0.0018	0.0231
3.0252	12.0	1284	3.1122	0.0026	0.0087	0.0008	0.001	0.0016	0.0029	0.0084	0.0284	0.0496	0.0005	0.0128	0.05	0.013	0.2194	0.0	0.0	0.0001	0.0205	0.0	0.0	0.0	0.008
3.0252	13.0	1391	3.1495	0.0028	0.0096	0.0005	0.0	0.0001	0.0031	0.0082	0.0285	0.0481	0.0	0.0173	0.0459	0.0136	0.2005	0.0	0.0	0.0001	0.0219	0.0	0.0	0.0001	0.0182
3.0252	14.0	1498	3.1443	0.0026	0.0083	0.0006	0.0	0.0001	0.0029	0.0091	0.0253	0.0486	0.0	0.0155	0.0466	0.0127	0.2036	0.0	0.0	0.0002	0.0344	0.0	0.0	0.0	0.0049
2.9223	15.0	1605	3.0269	0.0064	0.0181	0.0035	0.0035	0.0001	0.0072	0.0109	0.0318	0.0494	0.0029	0.012	0.0491	0.0314	0.2144	0.0	0.0	0.0001	0.0112	0.0	0.0	0.0002	0.0213
2.9223	16.0	1712	3.0312	0.0068	0.0178	0.0048	0.0015	0.0004	0.0077	0.0122	0.0323	0.0469	0.0013	0.0215	0.0419	0.033	0.1829	0.0	0.0	0.0002	0.0241	0.0	0.0	0.0008	0.0276
2.9223	17.0	1819	2.9839	0.0055	0.0158	0.0026	0.0027	0.0002	0.006	0.0118	0.0308	0.0527	0.0022	0.0236	0.0472	0.0267	0.2063	0.0	0.0	0.0001	0.0214	0.0	0.0	0.0006	0.0356
2.9223	18.0	1926	3.0200	0.0064	0.0186	0.0036	0.0005	0.0004	0.0072	0.0118	0.0295	0.0519	0.0004	0.0298	0.044	0.0311	0.1923	0.0	0.0	0.0001	0.0263	0.0	0.0	0.0008	0.0409
2.8252	19.0	2033	2.9895	0.0053	0.0166	0.0029	0.0025	0.0002	0.006	0.0113	0.0292	0.0475	0.002	0.021	0.0428	0.0262	0.1869	0.0	0.0	0.0001	0.0188	0.0	0.0	0.0004	0.032
2.8252	20.0	2140	3.0483	0.0038	0.0124	0.0018	0.0002	0.0001	0.0044	0.0111	0.0275	0.0431	0.0002	0.0172	0.0403	0.0188	0.1761	0.0	0.0	0.0001	0.0174	0.0	0.0	0.0002	0.0218
2.8252	21.0	2247	3.0509	0.0035	0.0112	0.0017	0.0	0.0001	0.004	0.0102	0.0314	0.0547	0.0	0.0124	0.0563	0.0174	0.2459	0.0	0.0	0.0	0.0107	0.0	0.0	0.0001	0.0169
2.8252	22.0	2354	2.9868	0.0039	0.0136	0.0015	0.001	0.0004	0.0042	0.0117	0.0353	0.064	0.0007	0.0304	0.0576	0.0183	0.2518	0.0	0.0	0.0001	0.0232	0.0	0.0	0.0009	0.0449
2.8252	23.0	2461	2.9752	0.0042	0.0137	0.0019	0.0015	0.0002	0.0047	0.0112	0.0337	0.0601	0.0011	0.021	0.0575	0.0204	0.2514	0.0	0.0	0.0002	0.0188	0.0	0.0	0.0004	0.0302
2.803	24.0	2568	2.9948	0.0042	0.013	0.0021	0.0015	0.0002	0.0046	0.0109	0.0309	0.0557	0.0011	0.0212	0.0526	0.0203	0.2297	0.0	0.0	0.0001	0.0174	0.0	0.0	0.0004	0.0316
2.803	25.0	2675	2.9797	0.0043	0.0139	0.0016	0.0015	0.0004	0.0047	0.0119	0.033	0.059	0.0011	0.0255	0.0541	0.0204	0.2365	0.0	0.0	0.0001	0.0214	0.0	0.0	0.001	0.0373
2.803	26.0	2782	2.9674	0.0042	0.0133	0.0022	0.002	0.0003	0.0046	0.0117	0.0336	0.0579	0.0017	0.0229	0.054	0.0201	0.236	0.0	0.0	0.0002	0.0152	0.0	0.0	0.0008	0.0382
2.803	27.0	2889	2.9539	0.0044	0.0141	0.0021	0.0025	0.0003	0.0047	0.012	0.0352	0.0592	0.0021	0.0232	0.0552	0.0207	0.241	0.0	0.0	0.0002	0.0192	0.0	0.0	0.0009	0.036
2.803	28.0	2996	2.9604	0.0042	0.0135	0.0021	0.002	0.0004	0.0046	0.0128	0.0347	0.0587	0.0016	0.0239	0.0575	0.0199	0.2338	0.0001	0.0038	0.0002	0.0205	0.0	0.0	0.0009	0.0356
2.7833	29.0	3103	2.9589	0.0044	0.0137	0.0023	0.0022	0.0004	0.0048	0.0129	0.035	0.0592	0.0018	0.0244	0.0577	0.0207	0.2347	0.0001	0.0038	0.0002	0.0205	0.0	0.0	0.001	0.0369
2.7833	30.0	3210	2.9593	0.0044	0.0137	0.0023	0.0022	0.0004	0.0048	0.0129	0.0353	0.0591	0.0018	0.0246	0.0575	0.0207	0.2338	0.0001	0.0038	0.0002	0.021	0.0	0.0	0.001	0.0369

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.2
• Datasets 2.19.1
• Tokenizers 0.19.1