rf_detr_finetuned_mobile_ui

This model is a fine-tuned version of Roboflow/rf-detr-medium on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 11.0488
• Map: 0.2827
• Map 50: 0.4193
• Map 75: 0.2913
• Map Small: 0.2021
• Map Medium: 0.2814
• Map Large: 0.3763
• Mar 1: 0.0609
• Mar 10: 0.3403
• Mar 100: 0.5668
• Mar Small: 0.4138
• Mar Medium: 0.5669
• Mar Large: 0.7317
• Map Group: 0.2092
• Mar 100 Group: 0.5979
• Map Image: 0.3334
• Mar 100 Image: 0.6295
• Map Rectangle: 0.2793
• Mar 100 Rectangle: 0.5245
• Map Text: 0.3089
• Mar 100 Text: 0.5151

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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: cosine
• num_epochs: 5

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 Group	Mar 100 Group	Map Image	Mar 100 Image	Map Rectangle	Mar 100 Rectangle	Map Text	Mar 100 Text
No log	1.0	417	11.5886	0.1303	0.2236	0.1298	0.081	0.1335	0.1877	0.0403	0.2307	0.4683	0.3218	0.486	0.5936	0.1244	0.4837	0.0958	0.514	0.1285	0.425	0.1725	0.4505
12.4114	2.0	834	11.3855	0.1893	0.3017	0.1918	0.117	0.1979	0.2564	0.0484	0.2862	0.514	0.3482	0.5158	0.6746	0.1305	0.5355	0.1979	0.5679	0.1964	0.4766	0.2324	0.4759
10.3603	3.0	1251	11.4305	0.2275	0.3547	0.2307	0.1535	0.2279	0.3118	0.0543	0.3069	0.527	0.3717	0.5347	0.6876	0.1602	0.5574	0.2617	0.5766	0.2249	0.4794	0.2631	0.4947
9.6684	4.0	1668	11.1439	0.2733	0.4068	0.2807	0.195	0.2753	0.3621	0.0603	0.3339	0.5593	0.4073	0.5588	0.722	0.2013	0.591	0.3158	0.6198	0.2733	0.5161	0.3028	0.5103
9.2140	5.0	2085	11.0488	0.2827	0.4193	0.2913	0.2021	0.2814	0.3763	0.0609	0.3403	0.5668	0.4138	0.5669	0.7317	0.2092	0.5979	0.3334	0.6295	0.2793	0.5245	0.3089	0.5151

Framework versions

• Transformers 5.9.0
• Pytorch 2.12.0+cu126
• Datasets 4.8.5
• Tokenizers 0.22.2