detr-resnet_finetuned

This model is a fine-tuned version of facebook/detr-resnet-50-dc5 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.6712
Model Preparation Time: 0.0124
Map: 0.0058
Map 50: 0.0128
Map 75: 0.0046
Map Small: 0.0042
Map Medium: 0.0081
Map Large: 0.0055
Mar 1: 0.0128
Mar 10: 0.0308
Mar 100: 0.0362
Mar Small: 0.0239
Mar Medium: 0.0494
Mar Large: 0.0448
Map Shirt, blouse: 0.0
Mar 100 Shirt, blouse: 0.0
Map Top, t-shirt, sweatshirt: 0.0
Mar 100 Top, t-shirt, sweatshirt: 0.0
Map Cardigan: 0.0
Mar 100 Cardigan: 0.0
Map Jacket: 0.0
Mar 100 Jacket: 0.0
Map Vest: 0.0
Mar 100 Vest: 0.0
Map Pants: 0.0
Mar 100 Pants: 0.0
Map Shorts: 0.0
Mar 100 Shorts: 0.0
Map Skirt: 0.0
Mar 100 Skirt: 0.0
Map Coat: 0.0
Mar 100 Coat: 0.0
Map Dress: 0.0681
Mar 100 Dress: 0.2918
Map Jumpsuit: 0.0
Mar 100 Jumpsuit: 0.0
Map Cape: 0.0
Mar 100 Cape: 0.0
Map Glasses: 0.0
Mar 100 Glasses: 0.0
Map Hat: 0.0
Mar 100 Hat: 0.0
Map Headband, head covering, hair accessory: 0.0
Mar 100 Headband, head covering, hair accessory: 0.0
Map Tie: 0.0
Mar 100 Tie: 0.0
Map Glove: 0.0
Mar 100 Glove: 0.0
Map Watch: 0.0
Mar 100 Watch: 0.0
Map Belt: 0.0
Mar 100 Belt: 0.0
Map Tights, stockings: 0.0
Mar 100 Tights, stockings: 0.0
Map Sock: 0.0
Mar 100 Sock: 0.0
Map Shoe: 0.1226
Mar 100 Shoe: 0.5358
Map Bag, wallet: 0.0
Mar 100 Bag, wallet: 0.0
Map Scarf: 0.0
Mar 100 Scarf: 0.0
Map Umbrella: 0.0
Mar 100 Umbrella: 0.0
Map Hood: 0.0
Mar 100 Hood: 0.0
Map Collar: 0.0
Mar 100 Collar: 0.0
Map Lapel: 0.0
Mar 100 Lapel: 0.0
Map Epaulette: 0.0
Mar 100 Epaulette: 0.0
Map Sleeve: 0.0368
Mar 100 Sleeve: 0.4661
Map Pocket: 0.0
Mar 100 Pocket: 0.0
Map Neckline: 0.0106
Mar 100 Neckline: 0.1905
Map Buckle: 0.0
Mar 100 Buckle: 0.0
Map Zipper: 0.0
Mar 100 Zipper: 0.0
Map Applique: 0.0
Mar 100 Applique: 0.0
Map Bow: 0.0
Mar 100 Bow: 0.0
Map Flower: 0.0
Mar 100 Flower: 0.0
Map Ribbon: 0.0
Mar 100 Ribbon: 0.0
Map Rivet: 0.0
Mar 100 Rivet: 0.0
Map Ruffle: 0.0
Mar 100 Ruffle: 0.0
Map Sequin: 0.0
Mar 100 Sequin: 0.0

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
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: linear
training_steps: 3000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Shirt, blouse	Mar 100 Shirt, blouse	Map Top, t-shirt, sweatshirt	Mar 100 Top, t-shirt, sweatshirt	Mar 100 Coat	Map Dress	Mar 100 Dress	Map Leg warmer	Mar 100 Leg warmer	Map Sock	Mar 100 Sock	Map Shoe	Mar 100 Shoe	Map Bag, wallet	Mar 100 Bag, wallet	Map Epaulette	Mar 100 Epaulette	Map Sleeve	Mar 100 Sleeve	Map Pocket	Mar 100 Pocket	Map Applique	Mar 100 Applique	Map Bead	Mar 100 Bead	Map Ruffle	Mar 100 Ruffle	Map Sequin	Mar 100 Sequin	Map Tassel	Mar 100 Tassel
6.151	0.5	50	6.2529	0.0124	0.0	0.0001	0.0	0.0	0.0	0.0001	0.0002	0.0025	0.0064	0.0002	0.0039	0.0148	0.0	0.0	0.0004	0.0467	0.0571	0.0003	0.0612	-1.0	-1.0	0.0	0.0	0.0001	0.0157	0.0001	0.0826	0.0	0.0	0.0	0.0009	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0	-1.0	-1.0
6.5354	1.0	100	5.5728	0.0124	0.0001	0.0003	0.0001	0.0001	0.0003	0.0001	0.0006	0.0021	0.004	0.0023	0.0064	0.0037	0.0	0.0	0.0007	0.0667	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0043	0.0955	0.0	0.0	0.0	0.0	0.0	0.0009	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0	-1.0	-1.0
4.9315	1.5	150	4.9862	0.0124	0.0002	0.0007	0.0001	0.0005	0.0005	0.0001	0.001	0.007	0.0114	0.0069	0.0204	0.0064	0.0004	0.02	0.0014	0.1167	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0078	0.3052	0.0	0.0174	0.0	0.0	0.0	0.0096	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0	-1.0	-1.0
4.4734	2.0	200	4.3909	0.0124	0.0005	0.0013	0.0003	0.0013	0.0009	0.0001	0.0014	0.0069	0.0102	0.0101	0.0182	0.0017	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0196	0.3746	0.0	0.0	0.0	0.0	0.0003	0.0443	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0
3.8903	2.5	250	4.0256	0.0124	0.0006	0.0017	0.0003	0.0011	0.0011	0.0001	0.0018	0.0073	0.0115	0.0095	0.0209	0.0037	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0239	0.394	0.0	0.0	0.0	0.0	0.0007	0.0765	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0
4.134	3.0	300	3.7973	0.0124	0.0009	0.0026	0.0004	0.0016	0.0015	0.0003	0.0025	0.008	0.0107	0.0094	0.0191	0.0025	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0356	0.3657	0.0	0.0	0.0	0.0	0.0011	0.0748	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0
3.3223	3.5	350	3.6230	0.0124	0.0011	0.0025	0.0009	0.0024	0.0017	0.0002	0.0024	0.0084	0.0115	0.0099	0.0186	0.0087	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0428	0.3276	0.0	0.0	0.0	0.0	0.0022	0.1452	0.0	0.0	0.0	0.0	-1.0	-1.0	0.0	0.0	0.0	0.0
3.8292	4.0	400	3.5537	0.0124	0.0012	0.0028	0.0009	0.0018	0.0018	0.0001	0.0024	0.0083	0.0109	0.0118	0.0175	0.0033	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.047	0.3754	0.0	0.0	0.0	0.0	0.0008	0.0713	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4.3096	4.5	450	3.4566	0.0124	0.0012	0.0033	0.0006	0.0027	0.0018	0.0002	0.0027	0.0085	0.0111	0.0098	0.0179	0.007	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0488	0.3209	0.0	0.0	0.0	0.0	0.0017	0.133	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.7201	5.0	500	3.3342	0.0124	0.0013	0.0035	0.0007	0.0019	0.0021	0.0004	0.0024	0.0102	0.0141	0.0121	0.0231	0.0107	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0528	0.4284	0.0	0.0	0.0	0.0	0.0023	0.1513	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.8089	5.5	550	3.2387	0.0124	0.0019	0.0047	0.0014	0.0023	0.0032	0.0005	0.0029	0.0119	0.0159	0.0124	0.0255	0.0149	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0744	0.4269	0.0	0.0	0.0	0.0	0.0041	0.2252	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.4317	6.0	600	3.2248	0.0124	0.0017	0.0043	0.001	0.0022	0.0027	0.0003	0.0021	0.0121	0.0162	0.0152	0.0265	0.0149	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.063	0.4187	0.0	0.0	0.0	0.0	0.0048	0.2461	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.5663	6.5	650	3.2108	0.0124	0.002	0.0052	0.001	0.0021	0.0037	0.0003	0.0027	0.0117	0.0169	0.0143	0.0278	0.0165	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0756	0.4112	0.0	0.0	0.0	0.0	0.0053	0.2826	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.3771	7.0	700	3.1788	0.0124	0.0024	0.0061	0.0013	0.0025	0.0048	0.0004	0.0037	0.0117	0.017	0.0134	0.0282	0.0128	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0937	0.4239	0.0	0.0	0.0	0.0	0.0051	0.2713	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.0818	7.5	750	3.1503	0.0124	0.0022	0.0051	0.0016	0.002	0.005	0.0002	0.0033	0.013	0.0183	0.0176	0.0293	0.0153	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0838	0.4552	0.0	0.0	0.0	0.0	0.0071	0.2957	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.246	8.0	800	3.1266	0.0124	0.0027	0.0064	0.0017	0.0026	0.0055	0.0002	0.0032	0.0132	0.0188	0.0169	0.0302	0.0165	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1021	0.4448	0.0	0.0	0.0	0.0	0.0087	0.3278	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3.1945	8.5	850	3.0969	0.0124	0.0029	0.0067	0.0019	0.0029	0.0059	0.0002	0.0036	0.015	0.0203	0.0176	0.033	0.0182	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1093	0.4709	0.0	0.0	0.0	0.0	0.0114	0.3617	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.9341	9.0	900	3.0639	0.0124	0.003	0.0068	0.0023	0.0036	0.0056	0.0004	0.0031	0.0137	0.0191	0.0149	0.0313	0.0128	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1145	0.4388	0.0	0.0	0.0	0.0	0.0083	0.3435	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.9812	9.5	950	3.0263	0.0124	0.0028	0.0066	0.0017	0.003	0.0057	0.0004	0.0039	0.014	0.0197	0.0156	0.0332	0.0165	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1028	0.4425	0.0	0.0	0.0	0.0	0.0091	0.3504	0.0	0.0	0.0049	0.0159	0.0	0.0	0.0	0.0
2.8798	10.0	1000	2.9767	0.0124	0.003	0.0068	0.0022	0.003	0.0066	0.0003	0.0034	0.0145	0.021	0.0159	0.0343	0.0186	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1096	0.4619	0.0	0.0	0.0	0.0	0.0119	0.3965	0.0	0.0	0.0003	0.0016	0.0	0.0	0.0	0.0
2.8805	10.5	1050	2.9619	0.0124	0.0032	0.0075	0.0026	0.0042	0.0065	0.0002	0.0038	0.0157	0.0227	0.0201	0.0366	0.0178	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1099	0.4866	0.0	0.0	0.0	0.0	0.0145	0.4183	0.0	0.0	0.0085	0.0254	0.0	0.0	0.0	0.0
3.0194	11.0	1100	2.9846	0.0124	0.0028	0.007	0.0022	0.0027	0.0059	0.0002	0.0042	0.0147	0.019	0.0131	0.0325	0.0174	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0963	0.4299	0.0	0.0	0.0	0.0	0.0137	0.34	0.0	0.0	0.0059	0.0095	0.0	0.0	0.0	0.0
3.6797	11.5	1150	2.9331	0.0124	0.0034	0.0079	0.0025	0.0033	0.0082	0.0003	0.005	0.017	0.0224	0.0182	0.0385	0.0169	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1041	0.4642	0.0	0.0	0.0	0.0	0.0174	0.3957	0.0	0.0	0.0187	0.0571	0.0	0.0	0.0	0.0
3.6285	12.0	1200	2.9318	0.0124	0.0034	0.008	0.0029	0.0036	0.0082	0.0002	0.0051	0.018	0.0233	0.0185	0.0396	0.0165	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1084	0.5119	0.0	0.0	0.0	0.0	0.0161	0.3678	0.0	0.0	0.0165	0.0746	0.0	0.0	0.0	0.0
3.1858	12.5	1250	2.9115	0.0124	0.0035	0.0077	0.0028	0.0039	0.0073	0.0002	0.005	0.0189	0.024	0.0191	0.0404	0.0202	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1128	0.4896	0.0	0.0	0.0	0.0	0.0193	0.4043	0.0	0.0	0.0106	0.0889	0.0	0.0	0.0	0.0
3.5477	13.0	1300	2.9734	0.0124	0.0043	0.0093	0.0037	0.0043	0.0088	0.0002	0.0059	0.0175	0.0219	0.02	0.0361	0.0227	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1287	0.4813	0.0	0.0	0.0	0.0	0.0297	0.3661	0.0	0.0	0.0166	0.0508	0.0	0.0	0.0	0.0
2.6609	13.5	1350	2.8948	0.0124	0.0038	0.009	0.0029	0.0039	0.0076	0.0003	0.0057	0.0191	0.0242	0.0204	0.0397	0.0236	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.1142	0.4993	0.0	0.0	0.0	0.0	0.0259	0.407	0.0	0.0	0.0141	0.0857	0.0	0.0	0.0	0.0
2.1238	14.0	1400	2.8598	0.0124	0.004	0.0097	0.003	0.0039	0.0069	0.0009	0.006	0.0198	0.0245	0.021	0.0401	0.0224	0.0	0.0	0.0	0.0	0.0	0.0153	0.0184	0.0	0.0	0.1103	0.5164	0.0	0.0	0.0	0.0	0.0249	0.3843	0.0	0.0	0.0135	0.0841	0.0	0.0	0.0	0.0
2.5572	14.5	1450	2.8251	0.0124	0.0039	0.0093	0.0026	0.0039	0.007	0.0008	0.0054	0.0201	0.025	0.0225	0.0402	0.0198	0.0	0.0	0.0	0.0	0.0	0.0119	0.0082	0.0	0.0	0.1148	0.5067	0.0	0.0	0.0	0.0	0.0239	0.3957	0.0	0.0	0.0099	0.1127	0.0	0.0	0.0	0.0
2.6484	15.0	1500	2.8246	0.0124	0.0039	0.0093	0.003	0.004	0.0074	0.0005	0.0057	0.0196	0.0244	0.022	0.0387	0.0235	0.0	0.0	0.0	0.0	0.0	0.0059	0.0082	0.0	0.0	0.1223	0.5157	0.0	0.0	0.0	0.0	0.0249	0.3974	0.0	0.0	0.0055	0.0778	0.0	0.0	0.0	0.0
2.6171	15.5	1550	2.8018	0.0124	0.004	0.01	0.0032	0.0037	0.0074	0.0008	0.0066	0.0211	0.0263	0.0197	0.0443	0.0258	0.0	0.0	0.0	0.0	0.0	0.0104	0.0306	0.0	0.0	0.1122	0.5052	0.0	0.0	0.0	0.0	0.0266	0.4165	0.0	0.0	0.0145	0.127	0.0	0.0	0.0	0.0
2.7484	16.0	1600	2.8278	0.0124	0.004	0.009	0.0031	0.0037	0.0086	0.0005	0.0064	0.0212	0.0256	0.0206	0.0435	0.0224	0.0	0.0	0.0	0.0	0.0	0.0048	0.0184	0.0	0.0	0.1153	0.4978	0.0	0.0	0.0	0.0	0.029	0.3957	0.0	0.0	0.0134	0.1397	0.0	0.0	0.0	0.0
2.2523	16.5	1650	2.7804	0.0124	0.0039	0.009	0.0031	0.0037	0.0087	0.0027	0.0063	0.023	0.0281	0.0201	0.0489	0.025	0.0	0.0	0.0	0.0	0.0	0.005	0.0224	0.0	0.0	0.1118	0.5127	0.0	0.0	0.0	0.0	0.0287	0.4539	0.0	0.0	0.0159	0.1651	0.0	0.0	0.0	0.0
2.792	17.0	1700	2.7744	0.0124	0.0039	0.0096	0.0028	0.0037	0.0074	0.0031	0.0059	0.0223	0.0279	0.0203	0.0474	0.0291	0.0	0.0	0.0	0.0	0.0	0.0112	0.0469	0.0	0.0	0.109	0.4731	0.0	0.0	0.0	0.0	0.0286	0.4713	0.0	0.0	0.0129	0.1524	0.0	0.0	0.0	0.0
2.9321	17.5	1750	2.7581	0.0124	0.004	0.0099	0.0031	0.0039	0.0074	0.0009	0.0065	0.0219	0.0274	0.0213	0.0451	0.0285	0.0	0.0	0.0	0.0	0.0	0.0106	0.0531	0.0	0.0	0.1172	0.4761	0.0	0.0	0.0	0.0	0.0244	0.4496	0.0	0.0	0.0114	0.1429	0.0	0.0	0.0	0.0
2.6387	18.0	1800	2.8029	0.0124	0.0042	0.0099	0.0027	0.0042	0.0075	0.001	0.0062	0.0203	0.0258	0.0206	0.0419	0.0306	0.0	0.0	0.0	0.0	0.0	0.0058	0.0347	0.0	0.0	0.1273	0.4858	0.0	0.0	0.0	0.0	0.0265	0.4296	0.0	0.0	0.0117	0.1063	0.0	0.0	0.0	0.0
3.8495	18.5	1850	2.7667	0.0124	0.0042	0.0104	0.0031	0.0039	0.0081	0.0012	0.0066	0.0223	0.0277	0.0223	0.044	0.0311	0.0	0.0	0.0	0.0	0.0	0.0111	0.0551	0.0	0.0	0.1227	0.5104	0.0	0.0	0.0	0.0	0.0262	0.4513	0.0	0.0	0.0126	0.1206	0.0	0.0	0.0	0.0
3.0583	19.0	1900	2.7503	0.0124	0.0043	0.0097	0.0035	0.0042	0.0076	0.0012	0.0076	0.0233	0.0291	0.0215	0.0467	0.0321	0.0	0.0	0.0	0.0	0.0	0.0165	0.0837	0.0	0.0	0.1205	0.5097	0.0	0.0	0.0	0.0	0.0275	0.4513	0.0	0.0	0.0136	0.1476	0.0	0.0	0.0	0.0
2.8411	19.5	1950	2.7440	0.0124	0.0043	0.0095	0.0036	0.0039	0.0078	0.0012	0.0079	0.0232	0.0288	0.0227	0.0454	0.0345	0.0	0.0	0.0	0.0	0.0	0.0144	0.0653	0.0	0.0	0.1194	0.5209	0.0	0.0	0.0	0.0	0.0298	0.4661	0.0	0.0	0.011	0.1302	0.0	0.0	0.0	0.0
3.0183	20.0	2000	2.7458	0.0124	0.0042	0.0098	0.0032	0.0035	0.0081	0.0018	0.0089	0.0241	0.0296	0.0218	0.046	0.0375	0.0	0.0	0.0	0.0	0.0	0.0227	0.102	0.0	0.0	0.1074	0.4955	0.0	0.0	0.0	0.0	0.0286	0.4539	0.0	0.0	0.0152	0.1619	0.0	0.0	0.0	0.0
2.8988	20.5	2050	2.7382	0.0124	0.0048	0.0111	0.0038	0.0038	0.0085	0.0022	0.0089	0.0255	0.0308	0.0241	0.0451	0.0365	0.0	0.0	0.0	0.0	0.0	0.0345	0.1245	0.0	0.0	0.1163	0.5336	0.0	0.0	0.0	0.0	0.0296	0.4557	0.0	0.0	0.0158	0.1476	0.0	0.0	0.0	0.0
2.5286	21.0	2100	2.7383	0.0124	0.0048	0.0108	0.0038	0.0042	0.0081	0.0036	0.0083	0.0257	0.0309	0.0248	0.0483	0.0339	0.0	0.0	0.0	0.0	0.0	0.0298	0.0959	0.0	0.0	0.1206	0.5366	0.0	0.0	0.0	0.0	0.0294	0.4461	0.0	0.0	0.0153	0.1873	0.0	0.0	0.0	0.0
3.2692	21.5	2150	2.7388	0.0124	0.0051	0.0113	0.0041	0.0042	0.0083	0.0025	0.0103	0.0264	0.0315	0.0236	0.0454	0.0355	0.0	0.0	0.0	0.0	0.0	0.0421	0.1653	0.0	0.0	0.1209	0.5172	0.0	0.0	0.0	0.0	0.0335	0.4443	0.0	0.0	0.0111	0.1635	0.0	0.0	0.0	0.0
2.7976	22.0	2200	2.7294	0.0124	0.0048	0.0111	0.0035	0.0037	0.0082	0.0046	0.0105	0.0253	0.0308	0.0237	0.0457	0.0347	0.0	0.0	0.0	0.0	0.0	0.0372	0.1571	0.0	0.0	0.1153	0.509	0.0	0.0	0.0	0.0	0.0318	0.4304	0.0	0.0	0.0119	0.1667	0.0	0.0	0.0	0.0
2.3505	22.5	2250	2.7265	0.0124	0.005	0.0114	0.0042	0.0044	0.008	0.0023	0.011	0.0275	0.0329	0.0236	0.048	0.0391	0.0	0.0	0.0	0.0	0.0	0.0377	0.198	0.0	0.0	0.1208	0.5209	0.0	0.0	0.0	0.0	0.0331	0.4565	0.0	0.0	0.0135	0.173	0.0	0.0	0.0	0.0
2.4853	23.0	2300	2.7120	0.0124	0.0052	0.0119	0.0041	0.0042	0.008	0.0031	0.0109	0.0285	0.0339	0.0233	0.0476	0.0432	0.0	0.0	0.0	0.0	0.0	0.0453	0.2306	0.0	0.0	0.1233	0.5194	0.0	0.0	0.0	0.0	0.0341	0.4704	0.0	0.0	0.0124	0.1714	0.0	0.0	0.0	0.0
2.3593	23.5	2350	2.7258	0.0124	0.0055	0.0128	0.004	0.0049	0.0072	0.0034	0.0116	0.0283	0.0329	0.0225	0.0455	0.0406	0.0	0.0	0.0	0.0	0.0	0.0549	0.2469	0.0	0.0	0.1235	0.4985	0.0	0.0	0.0	0.0	0.033	0.4357	0.0	0.0	0.0123	0.1667	0.0	0.0	0.0	0.0
2.4246	24.0	2400	2.7011	0.0124	0.0054	0.0125	0.0041	0.0044	0.0081	0.0028	0.0112	0.0297	0.0346	0.0245	0.0468	0.0412	0.0	0.0	0.0	0.0	0.0	0.0476	0.2592	0.0	0.0	0.1268	0.5269	0.0	0.0	0.0	0.0	0.0328	0.4609	0.0	0.0	0.0125	0.1698	0.0	0.0	0.0	0.0
6.0358	24.5	2450	2.7073	0.0124	0.0053	0.0123	0.0039	0.0041	0.0079	0.0029	0.0125	0.0293	0.034	0.0228	0.0462	0.042	0.0	0.0	0.0	0.0	0.0	0.0508	0.2755	0.0	0.0	0.1212	0.5037	0.0	0.0	0.0	0.0	0.0327	0.4478	0.0	0.0	0.0119	0.1667	0.0	0.0	0.0	0.0
2.7728	25.0	2500	2.7010	0.0124	0.0053	0.0122	0.0039	0.0047	0.0071	0.0042	0.0122	0.0294	0.0339	0.0221	0.048	0.0418	0.0	0.0	0.0	0.0	0.0	0.0506	0.2551	0.0	0.0	0.1207	0.5134	0.0	0.0	0.0	0.0	0.034	0.4565	0.0	0.0	0.0111	0.1667	0.0	0.0	0.0	0.0
2.5169	25.5	2550	2.6968	0.0124	0.0054	0.012	0.0045	0.004	0.0081	0.0032	0.0116	0.0289	0.0339	0.0235	0.0473	0.0402	0.0	0.0	0.0	0.0	0.0	0.058	0.2469	0.0	0.0	0.1204	0.5328	0.0	0.0	0.0	0.0	0.0333	0.4478	0.0	0.0	0.0112	0.1603	0.0	0.0	0.0	0.0
2.9971	26.0	2600	2.6801	0.0124	0.0057	0.0124	0.0046	0.004	0.0084	0.0045	0.0123	0.0303	0.0354	0.0234	0.0486	0.0439	0.0	0.0	0.0	0.0	0.0	0.0622	0.2816	0.0	0.0	0.1221	0.5381	0.0	0.0	0.0	0.0	0.0369	0.4548	0.0	0.0	0.0115	0.1762	0.0	0.0	0.0	0.0
2.3748	26.5	2650	2.6809	0.0124	0.0056	0.0123	0.0046	0.004	0.0083	0.0038	0.0121	0.0305	0.0354	0.0233	0.0487	0.0427	0.0	0.0	0.0	0.0	0.0	0.0597	0.2816	0.0	0.0	0.1236	0.5381	0.0	0.0	0.0	0.0	0.0362	0.4539	0.0	0.0	0.0106	0.1762	0.0	0.0	0.0	0.0
2.8506	27.0	2700	2.6779	0.0124	0.0057	0.012	0.0047	0.0042	0.0084	0.0038	0.0119	0.0302	0.0354	0.0235	0.0487	0.0419	0.0	0.0	0.0	0.0	0.0	0.0609	0.2653	0.0	0.0	0.125	0.5463	0.0	0.0	0.0	0.0	0.035	0.4635	0.0	0.0	0.0113	0.1746	0.0	0.0	0.0	0.0
2.53	27.5	2750	2.6831	0.0124	0.0057	0.0128	0.0047	0.0043	0.0085	0.0036	0.012	0.0304	0.0356	0.0234	0.0492	0.0433	0.0	0.0	0.0	0.0	0.0	0.0606	0.2776	0.0	0.0	0.1262	0.5455	0.0	0.0	0.0	0.0	0.0359	0.4609	0.0	0.0	0.0108	0.1762	0.0	0.0	0.0	0.0
2.1641	28.0	2800	2.6716	0.0124	0.006	0.013	0.0048	0.0042	0.0085	0.0052	0.0127	0.0309	0.0362	0.0243	0.0485	0.0445	0.0	0.0	0.0	0.0	0.0	0.0718	0.2939	0.0	0.0	0.1261	0.5463	0.0	0.0	0.0	0.0	0.036	0.4704	0.0	0.0	0.0111	0.1746	0.0	0.0	0.0	0.0
5.9674	28.5	2850	2.6713	0.0124	0.0058	0.0127	0.0045	0.0044	0.0079	0.0045	0.0125	0.0305	0.036	0.0251	0.0477	0.0445	0.0	0.0	0.0	0.0	0.0	0.0646	0.2857	0.0	0.0	0.1274	0.5507	0.0	0.0	0.0	0.0	0.0346	0.4643	0.0	0.0	0.0106	0.1762	0.0	0.0	0.0	0.0
2.6268	29.0	2900	2.6704	0.0124	0.0056	0.0124	0.0045	0.0041	0.0079	0.0043	0.0128	0.0302	0.0356	0.0238	0.0474	0.045	0.0	0.0	0.0	0.0	0.0	0.0621	0.2878	0.0	0.0	0.1227	0.5366	0.0	0.0	0.0	0.0	0.035	0.4609	0.0	0.0	0.0104	0.1746	0.0	0.0	0.0	0.0
2.5974	29.5	2950	2.6705	0.0124	0.0057	0.0124	0.0046	0.0041	0.0081	0.0054	0.0129	0.0309	0.0362	0.0238	0.0494	0.0449	0.0	0.0	0.0	0.0	0.0	0.0651	0.2939	0.0	0.0	0.1224	0.5366	0.0	0.0	0.0	0.0	0.0362	0.4661	0.0	0.0	0.0106	0.1889	0.0	0.0	0.0	0.0
2.4689	30.0	3000	2.6712	0.0124	0.0058	0.0128	0.0046	0.0042	0.0081	0.0055	0.0128	0.0308	0.0362	0.0239	0.0494	0.0448	0.0	0.0	0.0	0.0	0.0	0.0681	0.2918	0.0	0.0	0.1226	0.5358	0.0	0.0	0.0	0.0	0.0368	0.4661	0.0	0.0	0.0106	0.1905	0.0	0.0	0.0	0.0

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

osmanh/detr-resnet_finetuned

detr-resnet_finetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种