kvasir_seg_rtdetr_r18_test_fps

goodcasper/kvasir_seg_rtdetr_r18_test_fps

kvasir_seg_rtdetr_r18_test_fps is a object detection model from goodcasper. Use it when you need objects located in an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Object Detection
Library: transformers
License: apache-2.0
Model type: rt_detr
Access: Public
Created: Jun 22, 2025
Updated: Jun 22, 2025
SHA: f8b4ed8a

Base models

PekingU/rtdetr_r18vd_coco_o365

Model card

From the Hugging Face model README

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:

Loss: 6.4708
Map: 0.5375
Map 50: 0.7037
Map 75: 0.6008
Map Small: 0.0
Map Medium: 0.2762
Map Large: 0.5563
Mar 1: 0.6024
Mar 10: 0.8218
Mar 100: 0.9209
Mar Small: 0.0
Mar Medium: 0.78
Mar Large: 0.9325
Map Polyp: 0.5375
Mar 100 Polyp: 0.9209

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: 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 results

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

Framework versions

Transformers 4.53.0.dev0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Task: Object Detection
Library: transformers
Type: rt_detr
License: apache-2.0
Created: Jun 22, 2025
Updated: Jun 22, 2025