Fine-tuning on MM Grounding DINO needs tree pre-trained models?微调mm-g-dino需要三个预训练模型？

[zxt6174]

To train a mm_grodunding_dino ,we need to load both BERT and Swin two pre-trained models。 To fine-tune a mm_grounding_dino using my dataset, I need to load a pre-trained MM_Grounding_DINO and the config file ,which means I need to load BERT and Swin again.There are three models in total that I should load. Is this the right model fine-tuning process? Or maybe my method is wrong, how can I change it?

训练一个mm_grounding_dino需要调用BERT和Swin。 在自己的数据集上微调一个mm_grounding_dino，需要调用预训练的mm_g_dino权重和它的配置文件，但它的配置文件里需要调用BERT和Swin。一共需要调用三个预训练模型。 这是正确的微调流程吗？一定要调用三个模型吗？不能只调用一个mm_g_dino吗？还是我的方法出错了？

[simranbajaj06]

what are the end to end steps you follow to finetune MM_Grounding_DINO ? i finetune the MM_Grounding_DINO on 12 epochs and on 100 epochs as well , but its not giving the results even when i visualize the predictions there is no single bbox predicted on any image @zxt6174 @Czm369

[zxt6174]

I follwed the usage.md and the train.md to fine-tune on my datasets.

[simranbajaj06]

what is the size of data you take to finetune , and for how many epochs you trained on , and what time it took?