项目地址：https://github.com/microsoft/OmniParser
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https://github.com/xinyuwei-david/david-share.git/Multimodal-Models/
OmniParser
Box Threshold 和 IOU Threshold 是用于控制目标检测模型（YOLO模型）输出的两个重要参数。它们的具体含义如下：
Box Threshold（框阈值）：
这个参数用来设置检测到的边界框（bounding box）的置信度阈值。
边界框置信度是模型对检测到的目标的置信程度。置信度越高，模型越确定这个边界框内包含一个目标。
Box Threshold 的值介于0.01到1.0之间。当检测到的边界框置信度低于这个阈值时，这个边界框将被过滤掉，不会出现在最终的输出结果中。
例如，如果 Box Threshold 设置为0.05，那么置信度低于0.05的边界框将被忽略。
IOU Threshold（交并比阈值）：
IOU（Intersection over Union）是用来衡量两个边界框之间重叠程度的指标。
IOU Threshold 用来设置在进行非极大值抑制（NMS, Non-Maximum Suppression）时的阈值。NMS 是一种后处理步骤，用于移除重叠的边界框，只保留最有可能的那个。
IOU Threshold 的值介于0.01到1.0之间。当两个边界框的IOU值大于这个阈值时，置信度较低的边界框将被移除。
例如，如果 IOU Threshold 设置为0.1，那么当两个边界框的IOU值大于0.1时，置信度较低的那个边界框将被移除。
总结
Box Threshold：用于过滤掉置信度低于该阈值的边界框。
IOU Threshold：用于在非极大值抑制过程中移除重叠度高于该阈值的边界框。
这两个参数的设置可以帮助你调整目标检测的精度和召回率，以便在不同的应用场景中获得最佳的检测效果。
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本地目录结构：
(omni) root@a100vm:~/OmniParser/weights# ls -al ./*-rw-r--r-- 1 root root 319 Oct 28 11:20 ./convert_safetensor_to_pt.py./icon_caption_blip2:total 14628564drwxr-xr-x 2 root root 4096 Oct 28 13:40 .drwxr-xr-x 5 root root 4096 Oct 28 11:47 ..-rw-r--r-- 1 root root 1140 Oct 28 11:57 LICENSE-rw-r--r-- 1 root root 985 Oct 28 11:57 config.json-rw-r--r-- 1 root root 136 Oct 28 11:57 generation_config.json-rw-r--r-- 1 root root 4998300711 Oct 9 22:58 pytorch_model-00001-of-00002.bin-rw-r--r-- 1 root root 4998064248 Oct 24 14:31 pytorch_model-00001-of-00002.safetensors-rw-r--r-- 1 root root 2491516218 Oct 9 22:58 pytorch_model-00002-of-00002.bin-rw-r--r-- 1 root root 2491456448 Oct 24 14:31 pytorch_model-00002-of-00002.safetensors-rw-r--r-- 1 root root 121726 Oct 28 12:01 pytorch_model.bin.index.json./icon_caption_florence:total 1058612drwxr-xr-x 2 root root 4096 Oct 28 11:56 .drwxr-xr-x 5 root root 4096 Oct 28 11:47 ..-rw-r--r-- 1 root root 72204 Oct 28 11:40 LICENSE-rw-r--r-- 1 root root 5663 Oct 28 11:54 config.json-rw-r--r-- 1 root root 292 Oct 28 11:54 generation_config.json-rw-r--r-- 1 root root 1083916964 Oct 25 23:19 model.safetensors./icon_detect:total 18276drwxr-xr-x 2 root root 4096 Oct 28 11:52 .drwxr-xr-x 5 root root 4096 Oct 28 11:47 ..-rw-r--r-- 1 root root 400264 Oct 28 11:43 LICENSE-rw-r--r-- 1 root root 12222450 Oct 28 13:45 best.pt-rw-r--r-- 1 root root 6075790 Oct 24 23:11 model.safetensors-rw-r--r-- 1 root root 1087 Oct 28 11:52 model.yaml
在A100上推理的的话，肯定是很快的：
通过笔记本的CPU也能运行，但比GPU的运行速度要慢很多。
