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The Sub Networks have same configuration with the same parameters and weights.\n\nParameter updating is mirrored across both sub networks.It is used find the similarity of the inputs by comparing its feature vectors.\n*Triplet Loss* or *Euclidean Loss* can be used to find the distance between the feature vectors.\n\nHere, Euclidean Distance is used for Pairwise Distance.\n\nPaper Reference: https://arxiv.org/pdf/1707.02131.pdf\n\n![Structure](outputs/net_sign.png)\n\n## Sample Ouputs\n![img](outputs/img1.png)\n![img](outputs/img2.png)\n![img](outputs/img3.png)\n![img](outputs/img4.png)\n![img](outputs/img5.png)\n![img](outputs/img6.png)\n![img](outputs/img7.png)\n![img](outputs/img8.png)\n![img](outputs/img9.png)\n![img](outputs/img10.png)\n![img](outputs/img11.png)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvermavinay982%2Fsign-verification-siamese-net-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvermavinay982%2Fsign-verification-siamese-net-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvermavinay982%2Fsign-verification-siamese-net-pytorch/lists"}