rtdetr_v2_r50vd_finetuned

kweer/rtdetr_v2_r50vd_finetuned

rtdetr_v2_r50vd_finetuned is a object detection model from kweer. 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_v2
Access: Public
Created: Jul 12, 2026
Updated: Jul 12, 2026
SHA: 7445facd

Base models

PekingU/rtdetr_v2_r50vd

Model card

From the Hugging Face model README

rtdetr_v2_r50vd_finetuned

This model is a fine-tuned version of PekingU/rtdetr_v2_r50vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 10.6387
Map: 0.4061
Map 50: 0.5534
Map 75: 0.4829
Map Small: 0.0
Map Medium: 0.2252
Map Large: 0.4158
Mar 1: 0.4487
Mar 10: 0.6613
Mar 100: 0.7076
Mar Small: 0.0
Mar Medium: 0.5517
Mar Large: 0.7153
Map Bin: 0.7271
Mar Bin: 0.8391
Map Hand: 0.5457
Mar Hand: 0.8222
Map Not Bin: 0.1039
Mar Not Bin: 0.5818
Map Not Hand: 0.0011
Mar Not Hand: 0.5
Map Not Trash: 0.1534
Mar Not Trash: 0.531
Map Trash: 0.6285
Mar Trash: 0.7931
Map Trash Arm: 0.6832
Mar Trash Arm: 0.8857

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: 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: 500
num_epochs: 10
mixed_precision_training: Native AMP

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 Bin	Mar Bin	Map Hand	Mar Hand	Map Not Bin	Mar Not Bin	Map Not Hand	Mar Not Hand	Map Not Trash	Mar Not Trash	Map Trash	Mar Trash	Map Trash Arm	Mar Trash Arm
161.0519	1.0	38	99.2621	0.0062	0.0143	0.0049	0.0003	0.0069	0.0197	0.0371	0.0409	0.0056	0.042	0.0288	0.1805	0.0058	0.0238	0.0	0.0	-1.0	-1.0	0.0005	0.0119	0.0021	0.0294	0.0	0.0
92.3532	2.0	76	47.3904	0.0516	0.0922	0.0466	0.0004	0.0587	0.1054	0.2537	0.2905	0.0167	0.3019	0.159	0.6391	0.1369	0.3175	0.0027	0.125	-1.0	-1.0	0.0002	0.0262	0.0088	0.1853	0.0017	0.45
51.7429	3.0	114	25.9606	0.1533	0.2403	0.157	0.0157	0.1575	0.2074	0.3622	0.4956	0.0917	0.5201	0.3903	0.8149	0.3225	0.7413	0.0007	0.225	-1.0	-1.0	0.0019	0.1643	0.204	0.5779	0.0004	0.45
36.4871	4.0	152	19.0816	0.2521	0.341	0.2666	0.0468	0.2637	0.2935	0.4408	0.5294	0.1222	0.5761	0.6618	0.9126	0.4209	0.8302	0.1292	0.3125	-1.0	-1.0	0.0153	0.4167	0.2856	0.7044	0.0	0.0
29.2040	5.0	190	15.5496	0.3448	0.4748	0.378	0.0877	0.3642	0.3479	0.562	0.6404	0.2583	0.6813	0.6966	0.8724	0.6193	0.8254	0.1299	0.5375	-1.0	-1.0	0.1299	0.45	0.4911	0.7574	0.0022	0.4
23.4038	6.0	228	12.6222	0.3517	0.4786	0.3773	0.1253	0.3774	0.408	0.583	0.6895	0.325	0.7296	0.6996	0.8483	0.5462	0.7762	0.1317	0.5875	-1.0	-1.0	0.1239	0.5119	0.5973	0.7632	0.0112	0.65
19.3715	7.0	266	11.2698	0.4243	0.5764	0.4679	0.1593	0.4462	0.4577	0.6738	0.742	0.4	0.7771	0.7009	0.8402	0.5957	0.8143	0.1375	0.6875	-1.0	-1.0	0.1096	0.5024	0.5949	0.7574	0.407	0.85
16.9119	8.0	304	10.4336	0.4207	0.5675	0.4626	0.1672	0.4496	0.4705	0.6935	0.7121	0.3556	0.7486	0.7338	0.8425	0.6057	0.7921	0.1816	0.5875	-1.0	-1.0	0.2003	0.5167	0.5747	0.7338	0.2284	0.8
15.4101	9.0	342	10.4561	0.4657	0.6568	0.5378	0.1921	0.4825	0.5302	0.6822	0.7427	0.4472	0.7692	0.6963	0.8241	0.5938	0.7873	0.086	0.6625	-1.0	-1.0	0.209	0.5452	0.5992	0.7368	0.6096	0.9
14.3647	10.0	380	10.1698	0.4538	0.6065	0.5107	0.1667	0.4779	0.5295	0.6912	0.7414	0.4694	0.7663	0.7725	0.8736	0.5291	0.7841	0.1604	0.65	-1.0	-1.0	0.2104	0.5833	0.6027	0.7574	0.448	0.8

Framework versions

Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: rt_detr_v2
License: apache-2.0
Created: Jul 12, 2026
Updated: Jul 12, 2026