ModelListedExternal

Rtdetr v2 R101 Final

This model is a fine-tuned version of PekingU/rtdetrv2r101vd on an unknown dataset. It achieves the following results on the evaluation set: Loss: 7.8929 Map: 0.5152 Map 50: 0.8401 Map 75: 0.6058 Map Small: 0.4759...

Sourcehuggingface.co/dariacuna/rtdetr-v2-r101-final/tree/4d3cf36b4ca193249dafa85ea8716635269032ec

KindOtherBase modelPekingU/rtdetr_v2_r101vdVersionRevision 4d3cf36

Model source

Kind: Other
File size: 292 MB
Base model: PekingU/rtdetr_v2_r101vd
Version: vRevision 4d3cf36
Updated: Sep 16, 2026
Downloads: 3
Tasks: Object detection
Source: Hugging Face

Source description

rtdetr-v2-r101-final

This model is a fine-tuned version of PekingU/rtdetr_v2_r101vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 7.8929
Map: 0.5152
Map 50: 0.8401
Map 75: 0.6058
Map Small: 0.4759
Map Medium: 0.6186
Map Large: -1.0
Mar 1: 0.3149
Mar 10: 0.612
Mar 100: 0.612
Mar Small: 0.5802
Mar Medium: 0.6931
Mar Large: -1.0
Map Artemia: 0.5152
Mar 100 Artemia: 0.612

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: 5e-05
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: 300
num_epochs: 80

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 Artemia	Mar 100 Artemia
No log	1.0	250	14.4631	0.4356	0.7885	0.4451	0.3341	0.5718	-1.0	0.3442	0.5723	0.6283	0.5349	0.7562	-1.0	0.4356	0.6283
172.3915	2.0	500	8.3680	0.4508	0.8175	0.4463	0.3608	0.5821	-1.0	0.3611	0.5944	0.6374	0.5914	0.7036	-1.0	0.4508	0.6374
172.3915	3.0	750	7.9072	0.486	0.8594	0.5019	0.414	0.6008	-1.0	0.3741	0.6081	0.6498	0.6086	0.7073	-1.0	0.486	0.6498
13.7544	4.0	1000	8.1075	0.4813	0.8567	0.472	0.396	0.6033	-1.0	0.3723	0.5813	0.6243	0.5661	0.7036	-1.0	0.4813	0.6243
13.7544	5.0	1250	8.1847	0.4643	0.8472	0.479	0.3805	0.5916	-1.0	0.3698	0.5713	0.605	0.5419	0.692	-1.0	0.4643	0.605
11.8514	6.0	1500	8.0372	0.4639	0.8532	0.451	0.3786	0.5943	-1.0	0.3648	0.5782	0.6184	0.5608	0.6971	-1.0	0.4639	0.6184
11.8514	7.0	1750	8.0814	0.4674	0.8747	0.4302	0.391	0.5842	-1.0	0.3701	0.581	0.6106	0.5511	0.6927	-1.0	0.4674	0.6106
10.9011	8.0	2000	8.2653	0.4398	0.8378	0.4285	0.355	0.5941	-1.0	0.3601	0.5779	0.6112	0.5538	0.6905	-1.0	0.4398	0.6112
10.9011	9.0	2250	8.4868	0.4652	0.8473	0.4409	0.3851	0.5969	-1.0	0.3676	0.5894	0.6134	0.5543	0.6949	-1.0	0.4652	0.6134
9.8457	10.0	2500	8.6205	0.4452	0.8283	0.4359	0.3576	0.5895	-1.0	0.3601	0.5654	0.5745	0.507	0.6672	-1.0	0.4452	0.5745
9.8457	11.0	2750	8.5778	0.4562	0.8496	0.4099	0.3742	0.5877	-1.0	0.3583	0.566	0.5829	0.5075	0.6854	-1.0	0.4562	0.5829
9.3075	12.0	3000	8.7906	0.4436	0.8376	0.4091	0.3568	0.5928	-1.0	0.3592	0.5561	0.5592	0.4833	0.6628	-1.0	0.4436	0.5592
9.3075	13.0	3250	8.6636	0.4424	0.8438	0.4042	0.3615	0.5735	-1.0	0.3592	0.5564	0.5648	0.493	0.6635	-1.0	0.4424	0.5648
8.8441	14.0	3500	8.8710	0.4264	0.8051	0.4021	0.3356	0.5918	-1.0	0.3489	0.5623	0.5682	0.4978	0.6642	-1.0	0.4264	0.5682
8.8441	15.0	3750	9.2949	0.4249	0.7926	0.3942	0.3319	0.5922	-1.0	0.3495	0.5648	0.576	0.4973	0.6832	-1.0	0.4249	0.576
8.4733	16.0	4000	8.7027	0.4493	0.8336	0.4386	0.3685	0.5931	-1.0	0.3642	0.5548	0.5561	0.4785	0.662	-1.0	0.4493	0.5561
8.4733	17.0	4250	9.4468	0.4115	0.78	0.3713	0.3177	0.5912	-1.0	0.3417	0.5679	0.5891	0.5075	0.7007	-1.0	0.4115	0.5891
7.9193	18.0	4500	9.5585	0.3914	0.7409	0.3668	0.2885	0.5945	-1.0	0.3299	0.5835	0.596	0.5199	0.7	-1.0	0.3914	0.596
7.9193	19.0	4750	9.1970	0.414	0.7637	0.3963	0.3164	0.5856	-1.0	0.3402	0.5723	0.5804	0.4973	0.6942	-1.0	0.414	0.5804
7.8449	20.0	5000	9.4783	0.3908	0.7315	0.366	0.2966	0.5865	-1.0	0.3259	0.5701	0.5782	0.507	0.6752	-1.0	0.3908	0.5782
7.8449	21.0	5250	9.7154	0.3996	0.7527	0.3839	0.2972	0.5816	-1.0	0.3411	0.5645	0.566	0.4898	0.6708	-1.0	0.3996	0.566
7.4024	22.0	5500	9.3798	0.4209	0.7972	0.3731	0.3241	0.5837	-1.0	0.3374	0.571	0.5798	0.4925	0.6993	-1.0	0.4209	0.5798
7.4024	23.0	5750	9.3620	0.4082	0.7616	0.3938	0.3133	0.5793	-1.0	0.3442	0.5567	0.5592	0.486	0.6599	-1.0	0.4082	0.5592
7.0500	24.0	6000	9.8907	0.4099	0.7555	0.3939	0.3104	0.5943	-1.0	0.3386	0.553	0.5548	0.4747	0.6642	-1.0	0.4099	0.5548
7.0500	25.0	6250	9.7498	0.4134	0.7797	0.383	0.3178	0.5839	-1.0	0.348	0.5442	0.5452	0.4565	0.6664	-1.0	0.4134	0.5452
6.9048	26.0	6500	9.9386	0.4129	0.7763	0.3863	0.3119	0.5851	-1.0	0.3411	0.5477	0.5492	0.4629	0.6679	-1.0	0.4129	0.5492
6.9048	27.0	6750	10.0945	0.3989	0.748	0.3444	0.2991	0.5861	-1.0	0.3399	0.5564	0.5632	0.472	0.6883	-1.0	0.3989	0.5632
6.7264	28.0	7000	9.8866	0.4076	0.7616	0.3996	0.3075	0.5857	-1.0	0.343	0.5542	0.5567	0.4774	0.665	-1.0	0.4076	0.5567
6.7264	29.0	7250	9.7453	0.3964	0.7388	0.3569	0.2948	0.5835	-1.0	0.3352	0.5495	0.5517	0.4694	0.6642	-1.0	0.3964	0.5517
6.5454	30.0	7500	9.9308	0.405	0.7548	0.3789	0.302	0.5921	-1.0	0.3396	0.5424	0.5452	0.4597	0.662	-1.0	0.405	0.5452
6.5454	31.0	7750	9.8308	0.4182	0.7866	0.3799	0.322	0.5837	-1.0	0.3458	0.5511	0.5514	0.4688	0.665	-1.0	0.4182	0.5514
6.3073	32.0	8000	9.6804	0.4192	0.7808	0.3836	0.3246	0.5842	-1.0	0.3449	0.547	0.5483	0.4677	0.6591	-1.0	0.4192	0.5483
6.3073	33.0	8250	9.9787	0.408	0.7603	0.3814	0.3051	0.5923	-1.0	0.3461	0.5526	0.5533	0.4667	0.6723	-1.0	0.408	0.5533
6.2181	34.0	8500	10.1687	0.4028	0.7557	0.3845	0.3022	0.59	-1.0	0.3464	0.5561	0.5564	0.4737	0.6701	-1.0	0.4028	0.5564
6.2181	35.0	8750	10.1298	0.3873	0.7302	0.3667	0.2856	0.5852	-1.0	0.3421	0.5424	0.5449	0.4597	0.662	-1.0	0.3873	0.5449
6.0297	36.0	9000	10.0816	0.3652	0.6895	0.3325	0.2675	0.5632	-1.0	0.3349	0.543	0.547	0.4629	0.6628	-1.0	0.3652	0.547
6.0297	37.0	9250	11.2706	0.362	0.6849	0.3487	0.2587	0.575	-1.0	0.329	0.5511	0.5561	0.478	0.6635	-1.0	0.362	0.5561
6.0182	38.0	9500	10.2335	0.3826	0.7131	0.3568	0.2856	0.5688	-1.0	0.3346	0.5361	0.5377	0.4511	0.6562	-1.0	0.3826	0.5377
6.0182	39.0	9750	9.9745	0.3891	0.7372	0.3551	0.2936	0.5774	-1.0	0.3315	0.5461	0.5467	0.4656	0.6584	-1.0	0.3891	0.5467
5.7984	40.0	10000	10.3874	0.3801	0.7088	0.3675	0.2762	0.5912	-1.0	0.333	0.5433	0.5483	0.4591	0.6708	-1.0	0.3801	0.5483
5.7984	41.0	10250	10.5826	0.3795	0.7072	0.3694	0.2795	0.573	-1.0	0.334	0.5464	0.5477	0.4624	0.6642	-1.0	0.3795	0.5477
5.6742	42.0	10500	11.0054	0.3509	0.6604	0.3317	0.245	0.5826	-1.0	0.3259	0.5436	0.5467	0.4565	0.6708	-1.0	0.3509	0.5467
5.6742	43.0	10750	10.4947	0.3784	0.7003	0.3535	0.2857	0.5742	-1.0	0.3371	0.5421	0.543	0.4548	0.6642	-1.0	0.3784	0.543
5.5503	44.0	11000	10.2264	0.3893	0.7205	0.3594	0.2853	0.5909	-1.0	0.3377	0.5486	0.5489	0.4602	0.6708	-1.0	0.3893	0.5489
5.5503	45.0	11250	10.9303	0.3524	0.6437	0.3317	0.2591	0.5397	-1.0	0.3308	0.5455	0.5539	0.4667	0.6737	-1.0	0.3524	0.5539
5.4227	46.0	11500	10.5823	0.3508	0.6572	0.3243	0.2568	0.5511	-1.0	0.3246	0.5421	0.5458	0.4581	0.6664	-1.0	0.3508	0.5458
5.4227	47.0	11750	11.2549	0.3394	0.6428	0.3142	0.2564	0.5368	-1.0	0.3274	0.5517	0.553	0.4758	0.6591	-1.0	0.3394	0.553
5.2645	48.0	12000	10.5458	0.3934	0.7392	0.361	0.2914	0.5843...

Sources

1 source

Hugging FacePrimaryVersion 4d3cf36b4ca193249dafa85ea8716635269032ec

huggingface.co/dariacuna/rtdetr-v2-r101-final/tree/4d3cf36b4ca193249dafa85ea8716635269032ec

Verified Sep 16

Model artifacts

1 artifact

model.safetensors

safetensors · 292 MB · SHA-256 00f4abf470d6…e50e · Hugging Face

Download

Source excerpts

2 excerpts

ReadmeSep 16huggingface.co/dariacuna/rtdetr-v2-r101-final

--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r101vd tags: - generated_from_trainer model-index: - name: rtdetr-v2-r101-final results: [] ---  # rtdetr-v2-r101-final This model is a fine-tuned version of [PekingU/rtdetr_v2_r101vd](https://huggingface.co/PekingU/rtdetr_v2_r101vd) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.8929 - Map: 0.5152 - Map 50: 0.8401 - Map 75: 0.6058 - Map Small: 0.4759 - Map Medium: 0.6186 - Map Large: -1.0 - Mar 1: 0.3149 - Mar 10: 0.612 - Mar 100: 0.612 - Mar Small: 0.5802 - Mar Medium: 0.6931 - Mar Large: -1.0 - Map Artemia: 0.5152 - Mar 100 Artemia: 0.612 ## 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: 5e-05 - 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: 300 - num_epochs: 80 ### 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 Artemia | Mar 100 Artemia | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:| | No log | 1.0 | 250 | 14.4631 | 0.4356 | 0.7885 | 0.4451 | 0.3341 | 0.5718 | -1.0 | 0.3442 | 0.5723 | 0.6283 | 0.5349 | 0.7562 | -1.0 | 0.4356 | 0.6283 | | 172.3915 | 2.0 | 500 | 8.3680 | 0.4508 | 0.8175 | 0.4463 | 0.3608 | 0.5821 | -1.0 | 0.3611 | 0.5944 | 0.6374 | 0.5914 | 0.7036 | -1.0 | 0.4508 | 0.6374 | | 172.3915 | 3.0 | 750 | 7.9072 | 0.486 | 0.8594 | 0.5019 | 0.414 | 0.6008 | -1.0 | 0.3741 | 0.6081 | 0.6498 | 0.6086 | 0.7073 | -1.0 | 0.486 | 0.6498 | | 13.7544 | 4.0 | 1000 | 8.1075 | 0.4813 | 0.8567 | 0.4...

NoteSep 16huggingface.co/dariacuna/rtdetr-v2-r101-final

Source context: 3 downloads · 0 likes · Pipeline object-detection · Library transformers · Repo dariacuna/rtdetr-v2-r101-final