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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?
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
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?
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