Class

com.intel.analytics.bigdl.models.maskrcnn

MaskRCNNParams

Related Doc: package maskrcnn

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case class MaskRCNNParams(anchorSizes: Array[Float] = Array[Float](32, 64, 128, 256, 512), aspectRatios: Array[Float] = Array[Float](0.5f, 1.0f, 2.0f), anchorStride: Array[Float] = Array[Float](4, 8, 16, 32, 64), preNmsTopNTest: Int = 1000, postNmsTopNTest: Int = 1000, preNmsTopNTrain: Int = 2000, postNmsTopNTrain: Int = 2000, rpnNmsThread: Float = 0.7f, minSize: Int = 0, boxResolution: Int = 7, maskResolution: Int = 14, scales: Array[Float] = ..., samplingRatio: Int = 2, boxScoreThresh: Float = 0.05f, boxNmsThread: Float = 0.5f, maxPerImage: Int = 100, outputSize: Int = 1024, layers: Array[Int] = Array[Int](256, 256, 256, 256), dilation: Int = 1, useGn: Boolean = false) extends Product with Serializable

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Instance Constructors

  1. new MaskRCNNParams(anchorSizes: Array[Float] = Array[Float](32, 64, 128, 256, 512), aspectRatios: Array[Float] = Array[Float](0.5f, 1.0f, 2.0f), anchorStride: Array[Float] = Array[Float](4, 8, 16, 32, 64), preNmsTopNTest: Int = 1000, postNmsTopNTest: Int = 1000, preNmsTopNTrain: Int = 2000, postNmsTopNTrain: Int = 2000, rpnNmsThread: Float = 0.7f, minSize: Int = 0, boxResolution: Int = 7, maskResolution: Int = 14, scales: Array[Float] = ..., samplingRatio: Int = 2, boxScoreThresh: Float = 0.05f, boxNmsThread: Float = 0.5f, maxPerImage: Int = 100, outputSize: Int = 1024, layers: Array[Int] = Array[Int](256, 256, 256, 256), dilation: Int = 1, useGn: Boolean = false)

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. val anchorSizes: Array[Float]

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  5. val anchorStride: Array[Float]

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  6. final def asInstanceOf[T0]: T0

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  7. val aspectRatios: Array[Float]

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  8. val boxNmsThread: Float

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  9. val boxResolution: Int

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  10. val boxScoreThresh: Float

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  11. def clone(): AnyRef

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  12. val dilation: Int

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  13. final def eq(arg0: AnyRef): Boolean

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  14. def finalize(): Unit

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  15. final def getClass(): Class[_]

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  16. final def isInstanceOf[T0]: Boolean

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  17. val layers: Array[Int]

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  18. val maskResolution: Int

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  19. val maxPerImage: Int

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  20. val minSize: Int

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  21. final def ne(arg0: AnyRef): Boolean

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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  24. val outputSize: Int

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  25. val postNmsTopNTest: Int

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  26. val postNmsTopNTrain: Int

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  27. val preNmsTopNTest: Int

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  28. val preNmsTopNTrain: Int

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  29. val rpnNmsThread: Float

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  30. val samplingRatio: Int

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  31. val scales: Array[Float]

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  32. final def synchronized[T0](arg0: ⇒ T0): T0

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  33. val useGn: Boolean

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  34. final def wait(): Unit

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  35. final def wait(arg0: Long, arg1: Int): Unit

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  36. final def wait(arg0: Long): Unit

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