yolox / _base_ / yolox_cspdarknet.yml yolox_cspdarknet.yml 980 Bytes 一键复制 编辑 原始数据 按行查看 历史 Feng Ni 提交于 3年前 . add YOLOX doc and configs (#5731) 123456789101112131415161718192021222324252627282930313233343536373839404142 architecture: YOLOX norm_type: sync_bn ...
https://github.com/open-mmlab/mmdetection/blob/dev-3.x/configs/yolox/yolox_s_8xb8-300e_coco.py#L175 Config is like following. base_lr = 0.01 optim_wrapper = dict( type='OptimWrapper', optimizer=dict( type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4, nesterov=True), para...
yolox feat: mlflow logging integration (#1773) 7个月前 .gitignore fix(yolox): some bugs due to upgrade (#1602) 2年前 .pre-commit-config.yaml feat(exp): get_trainer method, add pre-commit (#1263) 3年前 .readthedocs.yaml build webpage of documentation (#352) ...
A complete Exp file is atyolox_base.py.It may be too long to write for every exp, but you can inherit the base Exp file and only overwrite the changed part. Let's take theVOC Exp fileas an example. We selectYOLOX-Smodel here, so we should change the network depth and width. VO...
链接初看YoloX代码,再对比之前看过的YoloV5的代码,简直是优雅和堆叠的强烈的对比。YoloV5代码晦涩,虽然也可以看懂,但需要花费很多的精力,而且就算看懂了,回头再看,还是得再捋一遍。YoloV5之于YoloX就是C++之于Python,前者功能强大,但内功不够容易走火入魔,后者优雅而简洁。建议读论文的人看YoloX。
I find it seems to spawn a lot of processes (16+). How can I control the number of process per device? Is there anything else I need to set besides self.data_num_workers in yolox/exp/yolox_base.py (which I already set to 2, but the number of process per device seem still ...
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ - YOLOX/yolox/models/yolo_head.py at ac58e0a5e68e57454b7b9ac822aced493b553c53 ·
请教一个问题,yolox输出解码过程如下图,对应的编码步骤在哪个位置 解码过程在yolo_head.py中的249-250行Member FateScript commented Oct 29, 2021 YOLOX/demo/MegEngine/python/models/yolo_head.py Line 177 in 38c633b def decode_outputs(self, outputs): Author Dandelion111 commented Oct 29, 2021...
I am currently conducting batch inference on the YOLOX model using the OpenVINO IR format. When the batch size is set to 1, the IR model's output shape is (1, 3549, 85). However, when the batch size is set to 'n', the output shape becomes (1, 3549 * n, 85). I anticipate ...
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