ShichengChen

import numpy as np

# Toy dataset
X = np.array([1, 2, 3, 4, 5])
y = np.array([1, 2, 3, 4, 5])

# Initial prediction: mean of y (Predictor Function)
initial_prediction = np.mean(y)
y_pred = np.array([initial_prediction] * len(y))

ShichengChen

<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>

<crowd-form
        id="form"
>
  <div id="errorBox"></div>

  <crowd-keypoint
          src="https://images.unsplash.com/photo-1605056545110-c2ef2253aa8c?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=2507&q=80"
          labels="['Ripe','qwe']"

ShichengChen

- dataset publication
  - readme modification following freihand readme
  - dataset website update
  
  
- organize server problems list and share with new adminstrators

- proper documentation of the data recording setup and the annotation system
  - write commends for anntation code
  - record a screen video for how to use the annotation system

ShichengChen

#include <iostream>
#include <map>
#include <unordered_map>
#include <list>
#include <utility>
#include <cassert>
using namespace std;

struct LRU{
  const int maxn=2;

ShichengChen

#include <bits/stdc++.h>
#include<ext/pb_ds/assoc_container.hpp>
typedef long long ll;
using namespace std;
using namespace __gnu_pbds;
#define lcnt (cnt<<1)
#define rcnt (cnt<<1|1)
#define vt vector
#define pb push_back
#define all(c) (c).begin(), (c).end()

ShichengChen

#define debug(x) cout << '>' << #x << ':' << (x) << endl;
#define debug2(y,z) cout << '>' << #y << ':' << (y) << " " << #z << ":" <<(z) << endl;

ShichengChen

#include <iostream>
#include <algorithm>
#include <set>
#include <vector>
#include <cstring>
#include <queue>
#include <bits/stdc++.h>
#include <cstdio>
#include <stdio.h>
using namespace std;

ShichengChen

@inproceedings{lin2017focal,
  title={Focal loss for dense object detection},
  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2980--2988},
  year={2017}
}