Finetune AutoSAM python scripts/main_autosam_seg.py --src_dir ${ACDC_folder} \ --data_dir ${ACDC_folder}/imgs/ --save_dir ./${output_dir} \ --b 4 --dataset ACDC --gpu ${gpu} \ --fold ${fold} --tr_size ${tr_size} --model_type ${model_type} --num_classes 4 This...
这篇是针对diffusion model的finetune, 文本指导生成图像. 一起拿来读是感觉一些tuning技巧也可拿到SAM上用. 摘要指出, 大模型落地到下游任务, 逃不开把小数据集的特定知识给到网络(局部Encoder, decoder也好, 丰富的prompt也好).Controlnet通过控制大模型学习特定下游任务的"依赖",实现特定知识传递. 小数据集也可tune...
To reduce the burden of fine tuning large foundation model and implement cost-efficient training scheme, we focus only on fine-tuning the additional CNN network and SAM decoder part. This strategy significantly reduces training time and achieves competitive results on publicly available dataset. The ...
In this study, we introduce a novel fine-tuning framework that leverages SAM's ability to bundle and process multiple prompts per image and seeks to improve SAM's performance in medical images. We first curated a medical image dataset that consists of CT scans of lesions in various organs, ...
Reparametrized fine-tuning算法:(1)Low-rank Decomposition (低秩分解);(2) LoRA Derivatives (LoRA派生物)。重新参数化表示在两种等效形式之间转换模型参数。具体来说,Reparametrized fine-tuning 在训练期间引入了额外的低秩可训练参数,然后将其与原始模型集成以进行推理。
with torch.no_grad(): sparse_embeddings, dense_embeddings = sam_model.prompt_encoder( points=None, boxes=box_torch, masks=None, ) 最后,我们可以生成遮罩。请注意,这里我们处于单掩码生成模式(与正常输出的3个掩码形成对比)。 low_res_masks, iou_predictions = sam_model.mask_decoder( image_embeddings...
finetuning_sam_for_flood_inundation_mapping Image Collection by api_data_owner Last Modified: July 11, 2024 0 comments, 1 views filepath = training_data.download(file_name=training_data.name) import zipfile with zipfile.ZipFile(filepath, 'r') as zip_ref: zip_ref.extractall(Path(filepath...
Finetune CNN decoder python scripts/main_feat_seg.py --src_dir ${ACDC_folder} \ --data_dir ${ACDC_folder}/imgs/ --save_dir ./${output_dir} \ --b 4 --dataset ACDC --gpu ${gpu} \ --fold ${fold} --tr_size ${tr_size} --model_type ${model_type} --num_classes 4 $...
Finetuning the SAM or FastSAM models on a custom dataset follows a similar approach to training other models in the Ultralytics YOLO family. Here are general steps: Prepare Your Data: You would first need to have your custom dataset labeled and structured in a format compatible with YOLOv8...
Finetune CNN decoder python scripts/main_feat_seg.py --src_dir ${ACDC_folder} \ --data_dir ${ACDC_folder}/imgs/ --save_dir ./${output_dir} \ --b 4 --dataset ACDC --gpu ${gpu} \ --fold ${fold} --tr_size ${tr_size} --model_type ${model_type} --num_classes 4 $...