vatsal-sodha

#function to check no of parity bits in genration of hamming code
#return no of parity bits required to append in given size of data word
def noOfParityBits(noOfBits):
	i=0
	while 2.**i <= noOfBits+i: # (power of 2 + parity bits laready  counted) that is for 4 bit of dataword requires 3 bit of parity bits
		i+=1
	return i
#function to genrate no of parity bits in while correction of hamming codes returns no of parity bits in given size of code word
def noOfParityBitsInCode(noOfBits):
	i=0

vatsal-sodha

#function to check no of parity bits in genration of hamming code
#return no of parity bits required to append in given size of data word
def noOfParityBits(noOfBits):
	i=0
	while 2.**i <= noOfBits+i: # (power of 2 + parity bits laready  counted) that is for 4 bit of dataword requires 3 bit of parity bits
		i+=1
	return i
#function to genrate no of parity bits in while correction of hamming codes returns no of parity bits in given size of code word
def noOfParityBitsInCode(noOfBits):
	i=0

vatsal-sodha

vatsal-sodha

"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python generate_tfrecord.py --csv_input=data/train_labels.csv  --output_path=train.record

  # Create test data:
  python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record
"""
from __future__ import division

vatsal-sodha

item {
  id: 1
  name: 'nodule'
} 

vatsal-sodha

# Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIIT Pets Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  faster_rcnn {
    num_classes: 37
    image_resizer {

vatsal-sodha

syntax = "proto2";

package object_detection.protos;

// Configuration proto for GridAnchorGenerator. See
// anchor_generators/grid_anchor_generator.py for details.
message GridAnchorGenerator {
   // Anchor height in pixels.
  optional int32 height = 1 [default = 256];

vatsal-sodha

syntax = "proto2";

package object_detection.protos;

import "object_detection/protos/faster_rcnn.proto";
import "object_detection/protos/ssd.proto";

// Top level configuration for DetectionModels.
message DetectionModel {
  oneof model {

vatsal-sodha

syntax = "proto2";

package object_detection.protos;

// Configuration proto for image resizing operations.
// See builders/image_resizer_builder.py for details.
message ImageResizer {
  oneof image_resizer_oneof {
    KeepAspectRatioResizer keep_aspect_ratio_resizer = 1;
    FixedShapeResizer fixed_shape_resizer = 2;