Comments (3)
Yes, you should resize images first. As I know, there is not such GPU that can support so large images.
from rcf.
Thanks for your reply.
What's the total loss and every dsn loss when you have finished the training?
i modified the solve.py, use 'rcf_pretrained_bsds.caffemodel' instead of '5stage-vgg.caffemodel',but the first iteration its total loss is 1400+, how can i know whether the loss is convergence?
I0915 09:39:08.640388 4169 net.cpp:284] Network initialization done.
I0915 09:39:08.640522 4169 solver.cpp:60] Solver scaffolding done.
I0915 09:39:09.026629 4169 solver.cpp:337] Iteration 0, Testing net (#0)
I0915 09:41:25.588088 4169 solver.cpp:228] Iteration 0, loss = 1436.18
I0915 09:41:25.588129 4169 solver.cpp:244] Train net output #0: dsn1_loss = 22.4332 (* 1 = 22.4332 loss)
I0915 09:41:25.588135 4169 solver.cpp:244] Train net output #1: dsn2_loss = 20.1456 (* 1 = 20.1456 loss)
I0915 09:41:25.588137 4169 solver.cpp:244] Train net output #2: dsn3_loss = 21.0044 (* 1 = 21.0044 loss)
I0915 09:41:25.588141 4169 solver.cpp:244] Train net output #3: dsn4_loss = 15.401 (* 1 = 15.401 loss)
I0915 09:41:25.588145 4169 solver.cpp:244] Train net output #4: dsn5_loss = 12.1914 (* 1 = 12.1914 loss)
I0915 09:41:25.588148 4169 solver.cpp:244] Train net output #5: fuse_loss = 14.5831 (* 1 = 14.5831 loss)
I0915 09:41:25.597915 4169 sgd_solver.cpp:106] Iteration 0, lr = 1e-06
from rcf.
The loss is different on different datasets. You can train and test the network for several times to find the secret of convergence for your dataset.
from rcf.
Related Issues (20)
- How to evaluate the NYUD? HOT 2
- pretrain model无法下载 HOT 1
- 定位公差maxDist HOT 2
- PASCAL VOC边缘集是怎么生成的
- NYUV2数据集获取 HOT 2
- 关于整体分割过程的问题
- 求pytorch版本的预训练ResNet模型
- 求pytorch版本的Resnet预训练模型
- Multicue数据集MaxDist HOT 3
- 关于matlab画精确率-召回率图的问题
- 您好,我想问一下,任意尺寸图片输入后会统一调整为多大? HOT 4
- 数据集 HOT 2
- 输出图片都是黑白的 HOT 4
- 使用pytorch版本跑NYUD数据集时需要更改那些地方的代码呢
- How to plot the curve on Multicue dataset HOT 1
- 关于轮廓细节信息的影响 HOT 2
- 关于multicue数据集的训练 HOT 2
- 关于NYUD-HHA-RGB的结果 HOT 7
- 在自定义数据集上进行evaluation HOT 5
- 我有点无法理解allBench和boundaryBench函数的原理,不知道OIS和ODS是如何计算的
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from rcf.