learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 10

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	38.7162	0.0281	0.0511	0.0258	0.0002	0.0185	0.0643	0.0476	0.113	0.1361	0.0087	0.0487	0.2792	0.0789	0.4432	0.0001	0.0228	0.0004	0.0237	0.0152	0.0692	0.0459	0.1218
No log	2.0	214	18.9832	0.0761	0.1526	0.0644	0.0353	0.0654	0.2102	0.1353	0.3126	0.3759	0.1266	0.3259	0.6436	0.1091	0.6194	0.0156	0.3266	0.0341	0.242	0.0191	0.2846	0.2023	0.4071
No log	3.0	321	13.6077	0.1752	0.336	0.1649	0.1135	0.1322	0.3377	0.2245	0.4264	0.4943	0.3222	0.4179	0.6847	0.2821	0.6622	0.0584	0.5165	0.1717	0.4232	0.054	0.3523	0.3099	0.5173
No log	4.0	428	13.1560	0.2263	0.4207	0.2104	0.123	0.1605	0.3983	0.2559	0.4724	0.5286	0.3789	0.4497	0.7202	0.4114	0.6941	0.0924	0.5797	0.1747	0.4004	0.1153	0.4169	0.3378	0.5516
36.8374	5.0	535	12.6611	0.2505	0.4767	0.2384	0.1732	0.1978	0.4396	0.2831	0.4849	0.5407	0.4167	0.4822	0.6991	0.3467	0.691	0.1534	0.5886	0.2309	0.4187	0.1644	0.4277	0.3568	0.5773
36.8374	6.0	642	12.5659	0.2576	0.4946	0.2407	0.2101	0.2032	0.4593	0.2874	0.4959	0.5556	0.4407	0.4877	0.7199	0.3378	0.6973	0.1598	0.5987	0.2355	0.4237	0.2048	0.4785	0.3499	0.58
36.8374	7.0	749	12.4556	0.2747	0.5177	0.2548	0.2354	0.2181	0.4835	0.2967	0.4947	0.5556	0.441	0.4979	0.7231	0.3715	0.6995	0.1808	0.5861	0.2433	0.4295	0.2428	0.4769	0.3353	0.5858
36.8374	8.0	856	12.2225	0.3073	0.5745	0.2947	0.2395	0.2472	0.5148	0.3079	0.5121	0.5598	0.4353	0.491	0.726	0.4049	0.7005	0.2623	0.5924	0.2578	0.4308	0.2615	0.4923	0.3501	0.5831
36.8374	9.0	963	12.1380	0.3083	0.5642	0.3073	0.2344	0.2466	0.5222	0.3045	0.5154	0.5556	0.4475	0.4803	0.7161	0.4156	0.7005	0.2392	0.5861	0.2685	0.4366	0.2581	0.4708	0.3602	0.584
14.6693	10.0	1070	12.2646	0.3089	0.5749	0.2984	0.2304	0.257	0.519	0.3092	0.5064	0.5535	0.4401	0.4906	0.7031	0.4125	0.7009	0.2577	0.5861	0.2494	0.4295	0.2601	0.4585	0.3649	0.5924

Framework versions

Transformers 4.54.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

nasiraziz321/rtdetr-r50-cppe5-finetune

rtdetr-r50-cppe5-finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nasiraziz321/
rtdetr-r50-cppe5-finetune