YOLOv5—Liteg.pt: |Baidu Drive|Google Drive| Baidu Drive Password:pogg v5lite-s model: TFLite Float32, Float16, INT8, Dynamic range quantization, ONNX, TFJS, TensorRT, OpenVINO IR FP32/FP16, Myriad Inference Engin Blob, CoreML https://github.com/PINTO0309/PINTO_model_zoo/tree/main/1...
padding,groups super().__init__() self.conv=Conv(c1*4,c2,k,s,p,g,act) defforward(self,x):#x(b,c,w,h)->y(b,4c,w/2,h/2) returnself.conv(torch.cat([x[...,::2,::2],x[...,1::2,::2],x[...,::2
v5lite-e.pt: |Baidu Drive|Google Drive| |──────ncnn-fp16: |Baidu Drive|Google Drive| |──────ncnn-int8: |Baidu Drive|Google Drive| └──────onnx-fp32: |Baidu Drive|Google Drive| v5lite-s.pt: |Baidu Drive|Google Drive| ...
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite. Contribute to ultralytics/yolov5 development by creating an account on GitHub.
Hello everyone. I converted a Yolov5 nano network from .pt format to .tflite format. The network only has two classes (Black Ball and Silver Ball). When using the .tflite file, the network stopped detecting Silver Ball. It only detects B...
现在你已经完成了“GitHub - ultralytics/yolov5: YOLOv5 頎巾簚 in PyTorch > ONNX > CoreML > TFLite”的实现过程。根据你的需求,你现在可以使用生成的CoreML或TFLite模型进行推理了。 注意:在以上步骤中,“path/to/your/pretrained/model.pt”需要替换为你选择的预训练模型的实际路径。
v5Lite-s.pt 3.4m shufflenetv2(Megvii) v5Lites-head Pytorch Arm-cpu v5Lite-s.binv5Lite-s.param 3.3m shufflenetv2 v5Lites-head ncnn Arm-cpu v5Lite-s-int8.binv5Lite-s-int8.param 1.7m shufflenetv2 v5Lites-head ncnn Arm-cpu v5Lite-s.mnn 3.3m shufflenetv2 v5Lites-head mnn Arm-cpu ...
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