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name: "ResNet50" | |
input: "data" | |
input_shape { | |
dim: 1 | |
dim: 3 | |
dim: 224 | |
dim: 224 | |
} |
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name: "ResNet-101" | |
layer { | |
name: 'input-data' | |
type: 'Python' | |
top: 'data' | |
top: 'im_info' | |
top: 'gt_boxes' | |
python_param { | |
module: 'roi_data_layer.layer' |
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name: "SimpleTriNet" | |
layer { | |
name: "data" | |
type: "Module" | |
top: "anchor" | |
top: "negative" | |
top: "positive" | |
module_param { | |
module: "triplet_layers" |
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train_net: "models/apc/ResNet18/faster_rcnn_end2end/train.prototxt" | |
base_lr: 0.1 | |
lr_policy: "step" | |
gamma: 0.1 | |
stepsize: 50000 | |
display: 20 | |
average_loss: 100 | |
# iter_size: 1 | |
momentum: 0.9 | |
weight_decay: 0.0005 |
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name: "fkp_net" | |
layers { | |
name: "data" | |
type: MEMORY_DATA | |
top: "data" | |
top: "label" | |
memory_data_param { | |
batch_size: 1783 #batch size, so how many prediction youu want to do at once. Best is "1", but higher number get better performance | |
channels: 1 | |
height: 96 |
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def get_session(): | |
config = tf.ConfigProto() | |
config.gpu_options.allow_growth = True | |
return tf.Session(config=config) | |
keras.backend.tensorflow_backend.set_session(get_session()) |
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#!/usr/bin/env python | |
import tensorflow as tf | |
from tensorflow.contrib.keras.api import keras | |
from tensorflow.contrib.keras.api.keras.models import Model, load_model | |
from tensorflow.contrib.keras.api.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Activation, Lambda | |
from tensorflow.contrib.keras.api.keras.datasets import mnist | |
from tensorflow.contrib.keras.api.keras.utils import to_categorical |
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