kvasir_seg_rtdetr_r18_test_fps

This model is a fine-tuned version of PekingU/rtdetr_r18vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

Model description

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 1
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: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 10

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Medium	Mar Large	Map Polyp	Mar 100 Polyp
188.8938	1.0	100	35.3914	0.045	0.0921	0.0405	0.0001	0.0612	0.1175	0.3521	0.6398	0.09	0.6705	0.045	0.6398
20.5545	2.0	200	13.3860	0.0707	0.1207	0.0633	0.0164	0.0763	0.2	0.5607	0.8398	0.63	0.8545	0.0707	0.8398
12.8462	3.0	300	8.8009	0.251	0.3922	0.2349	0.1155	0.263	0.3758	0.7171	0.8782	0.71	0.891	0.251	0.8782
11.3	4.0	400	8.9424	0.2968	0.4137	0.3193	0.2263	0.3046	0.3929	0.7687	0.9038	0.77	0.915	0.2968	0.9038
10.3262	5.0	500	7.8881	0.2819	0.4434	0.3041	0.2255	0.2897	0.4223	0.7374	0.8839	0.57	0.904	0.2819	0.8839
9.4336	6.0	600	7.9411	0.3048	0.4533	0.3209	0.2202	0.3159	0.4649	0.7934	0.9024	0.76	0.914	0.3048	0.9024
8.996	7.0	700	7.2079	0.4942	0.6964	0.5359	0.2982	0.5086	0.5531	0.7995	0.9171	0.74	0.9305	0.4942	0.9171
8.3482	8.0	800	6.5042	0.4987	0.6906	0.5471	0.2387	0.5154	0.5768	0.8095	0.9095	0.75	0.922	0.4987	0.9095
7.9702	9.0	900	6.4708	0.5375	0.7037	0.6008	0.2762	0.5563	0.6024	0.8218	0.9209	0.78	0.9325	0.5375	0.9209
7.7992	10.0	1000	6.3745	0.5317	0.7162	0.5988	0.2473	0.551	0.5953	0.8299	0.9223	0.76	0.935	0.5317	0.9223