Detectron | research platform for object detection research | Computer Vision library

# you can use your trained model
./demo.py logs/ --img 

# COCO Example: Mask R-CNN, ResNet50, 31.4 mAP@50:95
cd examples/coco
LOG_DIR=logs/20180730_081433
mkdir -p $LOG_DIR
pip install gdown
gdown https://drive.google.com/uc?id=1XC-Mx4HX0YBIy0Fbp59E  

# symlink the coco dataset
cd ~/github/maskrcnn-benchmark
mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
ln -s /path_to_coco_dataset/test2014 data  

# symlink the coco dataset
cd ~/github/maskrcnn-benchmark
mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
ln -s /path_to_coco_dataset/test2014 data  

# -*- coding: utf-8 -*-
# File: eval.py

import itertools
import json
import numpy as np
import os
import sys
from collections import namedtuple
from concurrent.futures import ThreadPoolExecutor
from contextlib import ExitStack
import cv2
import pyco  

# -*- coding: utf-8 -*-

import itertools
import numpy as np
import tensorflow as tf

from tensorpack import tfv1
from tensorpack.models import Conv2D, FixedUnPooling, MaxPooling, layer_register
from tensorpack.tfutils.argscope import argscope
from t  

# -*- coding: utf-8 -*-
# File: config.py

import numpy as np
import os
import pprint
import six

from tensorpack.utils import logger
from tensorpack.utils.gpu import get_num_gpu

__all__ = ['config', 'finalize_configs']


class AttrDict():

    _fre