rt_dterv2_finetuned_trashify_box_detector_v2

ironspiritjeff/rt_dterv2_finetuned_trashify_box_detector_v2

rt_dterv2_finetuned_trashify_box_detector_v2 is a object detection model from ironspiritjeff. 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: May 23, 2026
Updated: May 23, 2026
SHA: 82a2b2bb

Base models

PekingU/rtdetr_v2_r50vd

Model card

From the Hugging Face model README

rt_dterv2_finetuned_trashify_box_detector_v2

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: 9.1936
Map: 0.4075
Map 50: 0.5406
Map 75: 0.4698
Map Small: 0.0
Map Medium: 0.2646
Map Large: 0.4168
Mar 1: 0.4747
Mar 10: 0.655
Mar 100: 0.727
Mar Small: 0.0
Mar Medium: 0.5371
Mar Large: 0.7467
Map Bin: 0.7465
Mar Bin: 0.8931
Map Hand: 0.6137
Mar Hand: 0.85
Map Not Bin: 0.0631
Mar Not Bin: 0.5909
Map Not Hand: 0.0173
Mar Not Hand: 0.5833
Map Not Trash: 0.1557
Mar Not Trash: 0.5992
Map Trash: 0.6574
Mar Trash: 0.8157
Map Trash Arm: 0.5989
Mar Trash Arm: 0.7571

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: 10
eval_batch_size: 10
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: 0.05
num_epochs: 10
mixed_precision_training: Native AMP

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 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
70.3366	1.0	79	15.7799	0.1962	0.3146	0.2159	0.0	0.033	0.2041	0.2191	0.3769	0.475	0.0	0.1119	0.5208	0.4319	0.7592	0.4105	0.7592	0.0046	0.3857	-1.0	-1.0	0.0082	0.3247	0.322	0.6212	0.0	0.0
29.0653	2.0	158	11.0626	0.3132	0.4646	0.3527	0.0777	0.2919	0.3293	0.3264	0.4997	0.6157	0.15	0.5063	0.6452	0.6589	0.8577	0.4806	0.8398	0.0218	0.55	-1.0	-1.0	0.173	0.5123	0.5448	0.7345	0.0001	0.2
21.8803	3.0	237	10.2286	0.3782	0.5117	0.4184	0.0	0.1764	0.4025	0.4236	0.6629	0.7082	0.0	0.475	0.7457	0.7008	0.869	0.6114	0.8272	0.1019	0.4571	-1.0	-1.0	0.2253	0.5808	0.6134	0.7814	0.0166	0.7333
18.5955	4.0	316	9.3450	0.4378	0.6143	0.4689	0.1262	0.2491	0.4567	0.5044	0.6976	0.7425	0.125	0.529	0.7819	0.7591	0.8887	0.6202	0.8379	0.0712	0.5643	-1.0	-1.0	0.2249	0.5425	0.6371	0.7885	0.3142	0.8333
16.3647	5.0	395	8.9172	0.4421	0.6005	0.4883	0.1524	0.3045	0.4621	0.5725	0.7084	0.7496	0.25	0.5239	0.7865	0.7652	0.8887	0.6108	0.8301	0.0746	0.5357	-1.0	-1.0	0.2532	0.6096	0.6592	0.8	0.2895	0.8333
14.6126	6.0	474	8.9096	0.469	0.6421	0.5378	0.1856	0.2412	0.4887	0.5418	0.7114	0.7539	0.25	0.5727	0.7889	0.7734	0.8979	0.6092	0.832	0.0958	0.5857	-1.0	-1.0	0.208	0.6014	0.6618	0.8062	0.4661	0.8
13.3144	7.0	553	8.9221	0.4759	0.6607	0.5054	0.1519	0.3068	0.4924	0.5458	0.6888	0.729	0.25	0.517	0.7579	0.7874	0.8972	0.5777	0.8252	0.0603	0.5429	-1.0	-1.0	0.2516	0.5863	0.6703	0.7894	0.5079	0.7333
12.2178	8.0	632	9.0737	0.4492	0.6099	0.5189	0.1613	0.3054	0.4614	0.5546	0.7035	0.7448	0.275	0.5199	0.7815	0.7731	0.8958	0.5347	0.7922	0.0314	0.5929	-1.0	-1.0	0.1286	0.5616	0.6756	0.7929	0.5518	0.8333
11.3413	9.0	711	9.1222	0.4868	0.6666	0.5702	0.1264	0.3353	0.5095	0.5559	0.7157	0.7492	0.2	0.5437	0.7831	0.7794	0.9063	0.5725	0.8146	0.1389	0.6071	-1.0	-1.0	0.2331	0.574	0.6786	0.7929	0.5182	0.8
10.6519	10.0	790	9.1395	0.4819	0.6516	0.5577	0.1518	0.3198	0.4996	0.562	0.7148	0.7429	0.225	0.5307	0.7746	0.7731	0.9035	0.5624	0.8146	0.0927	0.5643	-1.0	-1.0	0.231	0.5534	0.6752	0.7885	0.557	0.8333

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: rt_detr_v2
License: apache-2.0
Created: May 23, 2026
Updated: May 23, 2026