learning_rate: 5e-05
train_batch_size: 16
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: linear
lr_scheduler_warmup_steps: 300
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Airplane	Map Apple	Map Backpack	Map Banana	Map Baseball bat	Map Baseball glove	Map Bear	Map Bed	Map Bench	Map Bicycle	Map Bird	Map Boat	Map Book	Map Bottle	Map Bowl	Map Broccoli	Map Bus	Map Cake	Map Car	Map Carrot	Map Cat	Map Cell phone	Map Chair	Map Clock	Map Couch	Map Cow	Map Cup	Map Dining table	Map Dog	Map Donut	Map Elephant	Map Fire hydrant	Map Fork	Map Frisbee	Map Giraffe	Map Hair drier	Map Handbag	Map Horse	Map Hot dog	Map Keyboard	Map Kite	Map Knife	Map Laptop	Map Large	Map Medium	Map Microwave	Map Motorcycle	Map Mouse	Map Orange	Map Oven	Map Parking meter	Map Person	Map Pizza	Map Potted plant	Map Refrigerator	Map Remote	Map Sandwich	Map Scissors	Map Sheep	Map Sink	Map Skateboard	Map Skis	Map Small	Map Snowboard	Map Spoon	Map Sports ball	Map Stop sign	Map Suitcase	Map Surfboard	Map Teddy bear	Map Tennis racket	Map Tie	Map Toaster	Map Toilet	Map Toothbrush	Map Traffic light	Map Train	Map Truck	Map Tv	Map Umbrella	Map Vase	Map Wine glass	Map Zebra	Mar 1	Mar 10	Mar 100	Mar 100 Airplane	Mar 100 Apple	Mar 100 Backpack	Mar 100 Banana	Mar 100 Baseball bat	Mar 100 Baseball glove	Mar 100 Bear	Mar 100 Bed	Mar 100 Bench	Mar 100 Bicycle	Mar 100 Bird	Mar 100 Boat	Mar 100 Book	Mar 100 Bottle	Mar 100 Bowl	Mar 100 Broccoli	Mar 100 Bus	Mar 100 Cake	Mar 100 Car	Mar 100 Carrot	Mar 100 Cat	Mar 100 Cell phone	Mar 100 Chair	Mar 100 Clock	Mar 100 Couch	Mar 100 Cow	Mar 100 Cup	Mar 100 Dining table	Mar 100 Dog	Mar 100 Donut	Mar 100 Elephant	Mar 100 Fire hydrant	Mar 100 Fork	Mar 100 Frisbee	Mar 100 Giraffe	Mar 100 Hair drier	Mar 100 Handbag	Mar 100 Horse	Mar 100 Hot dog	Mar 100 Keyboard	Mar 100 Kite	Mar 100 Knife	Mar 100 Laptop	Mar 100 Microwave	Mar 100 Motorcycle	Mar 100 Mouse	Mar 100 Orange	Mar 100 Oven	Mar 100 Parking meter	Mar 100 Person	Mar 100 Pizza	Mar 100 Potted plant	Mar 100 Refrigerator	Mar 100 Remote	Mar 100 Sandwich	Mar 100 Scissors	Mar 100 Sheep	Mar 100 Sink	Mar 100 Skateboard	Mar 100 Skis	Mar 100 Snowboard	Mar 100 Spoon	Mar 100 Sports ball	Mar 100 Stop sign	Mar 100 Suitcase	Mar 100 Surfboard	Mar 100 Teddy bear	Mar 100 Tennis racket	Mar 100 Tie	Mar 100 Toaster	Mar 100 Toilet	Mar 100 Toothbrush	Mar 100 Traffic light	Mar 100 Train	Mar 100 Truck	Mar 100 Tv	Mar 100 Umbrella	Mar 100 Vase	Mar 100 Wine glass	Mar 100 Zebra	Mar Large	Mar Medium	Mar Small
No log	1.0	370	9.4650	0.5295	0.6826	0.5707	0.7824	0.1443	0.2061	0.2278	0.5873	0.3108	0.9337	0.6263	0.4226	0.1765	1.0	0.2403	0.1317	0.4378	0.5302	0.487	0.8264	0.6871	0.5276	0.3177	0.8147	0.3335	0.3875	0.325	0.5228	0.7618	0.5623	0.3977	0.8948	-1.0	0.4286	-1.0	0.7265	0.9337	0.8705	-1.0	0.2144	0.9376	0.305	0.6782	0.4356	0.6093	0.7286	0.6695	0.4823	0.2535	0.4147	0.6485	0.2655	0.5475	0.9	0.5963	0.9282	0.2062	0.61	0.5045	0.5005	0.6634	0.7738	0.531	0.8327	0.3297	0.2751	0.6829	0.0732	0.2975	0.8495	0.5866	0.3841	0.2667	0.6098	0.285	-1.0	0.4233	0.0027	0.3928	0.6929	0.49	0.8589	0.5607	0.5855	0.6441	0.5759	0.4481	0.6711	0.7283	0.8571	0.5571	0.3667	0.7182	0.8375	0.4812	0.9333	0.95	0.5875	0.5	1.0	0.5327	0.4441	0.7074	0.852	0.7286	0.9167	0.85	0.6557	0.8	0.9077	0.5211	0.6292	0.6412	0.8727	0.8417	0.8057	0.6344	0.9273	-1.0	0.7778	-1.0	0.8143	0.9333	0.9222	-1.0	0.548	0.94	0.4833	0.875	0.75	0.9	0.8444	0.7667	0.6357	0.7	0.6211	0.8667	0.9	0.7068	0.94	0.475	0.8286	0.7143	0.7364	0.6667	0.7857	0.62	0.8333	0.5875	0.7857	0.62	0.4136	0.8875	0.7375	0.6727	0.8	0.7667	0.42	-1.0	0.6235	0.3	0.5125	0.8667	0.7357	0.9077	0.8059	0.7944	0.6875	0.7818	0.8283	0.6894	0.4893
15.7786	2.0	740	9.8112	0.5137	0.6598	0.5479	0.2697	0.4607	0.6598	0.4396	0.6637	0.723	0.4903	0.7122	0.8132	0.5881	0.7	0.2086	0.51	0.4924	0.6426	0.4431	0.6214	0.8008	0.9143	0.815	0.925	0.6606	0.8333	0.4001	0.7429	0.3062	0.5429	0.3713	0.5042	-1.0	-1.0	0.8457	0.875	0.45	0.9	0.3767	0.775	1.0	1.0	0.7966	0.9	0.8275	0.8727	0.9145	0.94	0.7663	0.7714	0.7638	0.85	0.3519	0.7222	0.9554	0.9667	0.6551	0.8045	0.8393	0.9333	0.1423	0.3833	0.4815	0.7412	0.1768	0.448	0.2763	0.46	0.5284	0.7563	0.9663	0.9667	0.3641	0.5562	0.3515	0.6857	0.2841	0.4227	0.3979	0.75	0.616	0.825	0.3174	0.4812	0.8699	0.9	0.5131	0.7091	0.6334	0.7444	0.4315	0.6519	0.6178	0.8	0.5324	0.7743	0.6943	0.7071	0.6698	0.8778	0.1486	0.64	0.5122	0.84	0.2135	0.7091	0.1239	0.5643	0.516	0.7909	0.2497	0.7053	0.5114	0.7071	0.5676	0.85	0.376	0.5778	0.8485	0.9	-1.0	-1.0	0.7341	0.825	0.3967	0.65	0.4688	0.8909	0.1926	0.44	0.5406	0.7833	0.3746	0.6375	0.3091	0.7	0.7874	0.9231	0.6304	0.8	0.6453	0.6833	0.4485	0.7048	0.7277	0.875	0.2773	0.4737	0.6257	0.9	0.3727	0.7583	-1.0	-1.0	0.5839	0.64	0.7337	0.8857	0.1202	0.4075	0.4162	0.6059	0.433	0.7278	0.5	0.6667	0.16	0.8	-1.0	-1.0	0.0	0.0
15.2267	3.0	1110	9.5181	0.5272	0.6782	0.5667	0.2848	0.4743	0.6746	0.4334	0.6705	0.728	0.4981	0.6968	0.8302	0.5821	0.7002	0.2675	0.46	0.5146	0.6516	0.403	0.5786	0.8202	0.9429	0.8327	0.9167	0.7368	0.8667	0.5293	0.7643	0.2705	0.5184	0.3679	0.5229	-1.0	-1.0	0.8428	0.85	0.1667	1.0	0.3367	0.675	1.0	1.0	0.8129	0.8923	0.8235	0.9091	0.9148	0.96	0.7807	0.8	0.7585	0.8542	0.5243	0.8	0.9346	0.9667	0.6425	0.8091	0.8826	0.9222	0.1826	0.4333	0.5598	0.8059	0.2344	0.512	0.1997	0.3	0.5124	0.75	0.9554	0.9667	0.3572	0.5813	0.53	0.7143	0.3592	0.4727	0.4659	0.7444	0.6363	0.8625	0.2784	0.4313	0.7041	0.9	0.4682	0.7273	0.5722	0.6667	0.4555	0.6444	0.66	0.7375	0.5997	0.7829	0.7199	0.7857	0.6277	0.8556	0.1853	0.58	0.4361	0.824	0.199	0.7227	0.1313	0.55	0.4585	0.8182	0.3368	0.6316	0.5568	0.7286	0.503	0.8	0.3306	0.5056	0.9063	0.93	-1.0	-1.0	0.7817	0.825	0.4112	0.6573	0.5598	0.8818	0.176	0.52	0.4713	0.9	0.426	0.7125	0.4154	0.7	0.8263	0.9154	0.7954	0.8444	0.7102	0.75	0.5149	0.7143	0.6499	0.85	0.3142	0.4895	0.699	0.8333	0.5545	0.875	-1.0	-1.0	0.5914	0.64	0.6574	0.8429	0.1111	0.4269	0.4465	0.6	0.5349	0.7556	0.4168	0.6667	0.1367	0.8	-1.0	-1.0	0.0	0.0
15.2267	4.0	1480	9.4856	0.5394	0.6966	0.5711	0.2738	0.4944	0.6885	0.4664	0.6739	0.7308	0.504	0.6963	0.8268	0.5779	0.7096	0.2869	0.47	0.5224	0.659	0.435	0.6143	0.832	0.8857	0.8937	0.9333	0.7535	0.8889	0.4417	0.775	0.3023	0.5755	0.3554	0.5042	-1.0	-1.0	0.8572	0.8625	0.3	0.9	0.4156	0.675	1.0	1.0	0.8174	0.8846	0.9042	0.9182	0.8901	0.94	0.7839	0.8429	0.7192	0.85	0.4684	0.7667	0.9345	0.9667	0.6268	0.7591	0.8757	0.9444	0.2231	0.4556	0.5582	0.7824	0.2454	0.524	0.3936	0.44	0.5985	0.75	0.9554	0.9667	0.3541	0.6187	0.4877	0.7143	0.3512	0.4909	0.487	0.7167	0.5927	0.85	0.3282	0.4812	0.8705	0.9333	0.3872	0.6545	0.6002	0.7	0.4458	0.6444	0.636	0.75	0.5973	0.8114	0.7317	0.7857	0.6544	0.8778	0.0935	0.44	0.491	0.828	0.2332	0.7636	0.1781	0.5643	0.4309	0.7182	0.3405	0.6421	0.5539	0.65	0.6525	0.85	0.3568	0.5556	0.9147	0.95	-1.0	-1.0	0.7341	0.825	0.4212	0.624	0.488	0.8909	0.1858	0.485	0.4515	0.8833	0.3833	0.6187	0.443	0.7059	0.8287	0.9077	0.7827	0.8556	0.7686	0.8	0.5286	0.7429	0.7607	0.875	0.2528	0.4737	0.5245	0.7333	0.5762	0.8667	-1.0	-1.0	0.5976	0.69	0.6893	0.8143	0.1171	0.4398	0.4157	0.5765	0.5086	0.7333	0.6634	0.6667	0.1333	0.8	-1.0	-1.0	0.002	0.3
14.0763	5.0	1850	9.3999	0.5327	0.6888	0.5731	0.2833	0.4621	0.687	0.4388	0.6686	0.7222	0.4796	0.6884	0.8249	0.5836	0.707	0.2904	0.54	0.5271	0.6508	0.448	0.5857	0.84	0.9143	0.8964	0.925	0.7502	0.8	0.4752	0.775	0.296	0.5673	0.3745	0.5104	-1.0	-1.0	0.8178	0.8375	0.1125	0.9	0.4415	0.7	1.0	1.0	0.8285	0.8769	0.9249	0.9273	0.9026	0.94	0.7792	0.8143	0.7586	0.8667	0.508	0.7778	0.9337	0.9333	0.6353	0.7636	0.875	0.9	0.2317	0.4111	0.5165	0.7882	0.2554	0.536	0.2376	0.32	0.5525	0.7437	0.9554	0.9667	0.3611	0.6125	0.467	0.7714	0.3292	0.4636	0.4922	0.7278	0.6464	0.85	0.3074	0.4187	0.8671	0.9	0.4045	0.6273	0.5949	0.7111	0.4412	0.6444	0.6175	0.75	0.6221	0.8314	0.7218	0.7714	0.6039	0.8556	0.1839	0.44	0.5203	0.816	0.2206	0.7318	0.1947	0.5643	0.4644	0.7455	0.2901	0.6211	0.5369	0.7357	0.5171	0.8	0.3502	0.5556	0.9161	0.94	-1.0	-1.0	0.7348	0.85	0.4139	0.6271	0.4798	0.8636	0.22	0.51	0.4983	0.9167	0.3962	0.6281	0.422	0.6765	0.82	0.9077	0.7589	0.8556	0.7566	0.8	0.5195	0.7476	0.7419	0.875	0.2701	0.4895	0.6594	0.8	0.5557	0.8333	-1.0	-1.0	0.6079	0.63	0.6643	0.8714	0.11	0.4355	0.3861	0.5706	0.4955	0.7722	0.4891	0.5667	0.0662	0.8	-1.0	-1.0	0.0	0.0

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.4.2
Tokenizers 0.22.1

benjamintli/rt-detr-v2_coco2017-5k

rt-detr-v2_coco2017-5k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

benjamintli/
rt-detr-v2_coco2017-5k