rt-detr-v2_barcode-detection

This model is a fine-tuned version of PekingU/rtdetr_v2_r18vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 5.0340
Map: 0.6583
Map 50: 0.8036
Map 75: 0.7156
Map Small: 0.2754
Map Medium: 0.7152
Map Large: 0.747
Mar 1: 0.3589
Mar 10: 0.833
Mar 100: 0.8673
Mar Small: 0.6183
Mar Medium: 0.8656
Mar Large: 0.8872
Map Barcode: 0.6583
Mar 100 Barcode: 0.8673

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: 16
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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Barcode	Map Large	Map Medium	Map Small	Mar 1	Mar 10	Mar 100	Mar 100 Barcode	Mar Large	Mar Medium	Mar Small
7.43	1.0	1636	5.0547	0.623	0.7756	0.6841	0.623	0.6594	0.6674	0.2648	0.3542	0.8117	0.8521	0.8521	0.8749	0.8552	0.5325
6.9538	2.0	3272	4.9508	0.6297	0.7829	0.6878	0.6297	0.7109	0.6449	0.261	0.3612	0.8238	0.8604	0.8604	0.8794	0.8624	0.5972
6.6733	3.0	4908	5.0056	0.6553	0.8073	0.7122	0.6553	0.7359	0.6782	0.2319	0.3481	0.8201	0.8542	0.8542	0.8712	0.8573	0.6107
6.5783	4.0	6544	5.0493	0.6524	0.808	0.711	0.6524	0.7472	0.6782	0.2391	0.3581	0.8268	0.8629	0.8629	0.8819	0.8639	0.6066
6.4986	5.0	8180	5.0208	0.6944	0.8578	0.7542	0.6944	0.7651	0.7135	0.2808	0.3706	0.8307	0.8611	0.8611	0.879	0.863	0.6131
6.4056	6.0	9816	5.0116	0.6843	0.8399	0.7445	0.6843	0.7607	0.7126	0.3026	0.371	0.8374	0.8664	0.8664	0.8864	0.8646	0.6176
6.4074	7.0	11452	5.0229	0.653	0.7989	0.7106	0.2768	0.7112	0.731	0.3513	0.8326	0.8694	0.619	0.8675	0.8896	0.653	0.8694
6.2576	8.0	13088	5.0340	0.6583	0.8036	0.7156	0.2754	0.7152	0.747	0.3589	0.833	0.8673	0.6183	0.8656	0.8872	0.6583	0.8673

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.4.2
Tokenizers 0.22.1

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rt-detr-v2_barcode-detection

This model is a fine-tuned version of PekingU/rtdetr_v2_r18vd on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 5.0340

Map: 0.6583

Map 50: 0.8036

Map 75: 0.7156

Map Small: 0.2754

Map Medium: 0.7152

Map Large: 0.747

Mar 1: 0.3589

Mar 10: 0.833

Mar 100: 0.8673

Mar Small: 0.6183

Mar Medium: 0.8656

Mar Large: 0.8872

Map Barcode: 0.6583

Mar 100 Barcode: 0.8673

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05

train_batch_size: 16

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Barcode	Map Large	Map Medium	Map Small	Mar 1	Mar 10	Mar 100	Mar 100 Barcode	Mar Large	Mar Medium	Mar Small
7.43	1.0	1636	5.0547	0.623	0.7756	0.6841	0.623	0.6594	0.6674	0.2648	0.3542	0.8117	0.8521	0.8521	0.8749	0.8552	0.5325
6.9538	2.0	3272	4.9508	0.6297	0.7829	0.6878	0.6297	0.7109	0.6449	0.261	0.3612	0.8238	0.8604	0.8604	0.8794	0.8624	0.5972
6.6733	3.0	4908	5.0056	0.6553	0.8073	0.7122	0.6553	0.7359	0.6782	0.2319	0.3481	0.8201	0.8542	0.8542	0.8712	0.8573	0.6107
6.5783	4.0	6544	5.0493	0.6524	0.808	0.711	0.6524	0.7472	0.6782	0.2391	0.3581	0.8268	0.8629	0.8629	0.8819	0.8639	0.6066
6.4986	5.0	8180	5.0208	0.6944	0.8578	0.7542	0.6944	0.7651	0.7135	0.2808	0.3706	0.8307	0.8611	0.8611	0.879	0.863	0.6131
6.4056	6.0	9816	5.0116	0.6843	0.8399	0.7445	0.6843	0.7607	0.7126	0.3026	0.371	0.8374	0.8664	0.8664	0.8864	0.8646	0.6176
6.4074	7.0	11452	5.0229	0.653	0.7989	0.7106	0.2768	0.7112	0.731	0.3513	0.8326	0.8694	0.619	0.8675	0.8896	0.653	0.8694
6.2576	8.0	13088	5.0340	0.6583	0.8036	0.7156	0.2754	0.7152	0.747	0.3589	0.833	0.8673	0.6183	0.8656	0.8872	0.6583	0.8673

Framework versions

Transformers 4.57.3

Pytorch 2.9.0+cu126

Datasets 4.4.2

Tokenizers 0.22.1

benjamintli/rt-detr-v2_barcode-detection

rt-detr-v2_barcode-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rt-detr-v2_barcode-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions