fast-rcnn | Fast R-CNN is a fast framework | Machine Learning library
kandi X-RAY | fast-rcnn Summary
kandi X-RAY | fast-rcnn Summary
Fast R-CNN is a fast framework for object detection with deep ConvNets. Fast R-CNN. Fast R-CNN was initially described in an arXiv tech report and later published at ICCV 2015.
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Top functions reviewed by kandi - BETA
- Add bounding boxes to the ROI table
- Compute the ground - truth targets for the ground - truth image
- List of roidb
- Set the roidb handler
- Load configuration from file
- Recursively merge config into b
- Parse command line arguments
- Prepare roidb data
- Create a config dictionary from a list
- Append flipped images
- Create a selective search using selective search
- Get an imdb dataset
- Adds a path to sys path
- Set competition mode
fast-rcnn Key Features
fast-rcnn Examples and Code Snippets
Lines of Code : 25License : Non-SPDX (NOASSERTION)
Copy"Unknown layer type: Python"
WITH_PYTHON_LAYER := 1
cd $ROOT_DIR/py-faster-rcnn/caffe-fast-rcnn/
make clean
make -j8 && make pycaffe
fatal error: caffe/proto/caffe.pb.h: No such file or directory
#include "caffe/proto/caffe.pb.h"
# Make sure to clone with --recursive
git clone --recursive https://github.com/cguindel/lsi-faster-rcnn.git
git submodule update --init --recursive
cd $FRCN_ROOT/lib
gedit setup.py
extra_compile_args={'gcc': ["-Wno-unused-function"],
export SERVER_NAME=virginia-dl ## or SERVER_NAME=ubuntu@ip-address
## copy files to server (dish data, pre trainned h5 file)
scp -r dish_server/* ${SERVER_NAME}:/home/ubuntu
ssh ${SERVER_NAME}
(server) > tmux new -s train
(server - tmux) > sou Community Discussions
Trending Discussions on fast-rcnn
QUESTION
I'm reading a paper about Fast-RCNN model.
In the paper section 2.3 part of 'SGD hyper-parameters', it said that All layers use a per-layer learning rate of 1 for weights and 2 for biases and a global learning rate of 0.001
Is 'per-layer learning rate' same as 'layer-specific learning rate' that give different learning rate by layers? If so, I can't understand how they('per-layer learning rate' and 'global learning rate') can be apply at the same time?
I found the example of 'layer-specific learning rate' in pytorch.
...ANSWER
Answered 2021-Oct-01 at 17:46The per-layer terminology in that paper is slightly ambiguous. They aren't referring to the layer-specific learning rates.
All layers use a per-layer learning rate of 1 for weights and 2 for biases and a global learning rate of 0.001.
The concerned statement is w.r.t. Caffe framework in which Fast R-CNN was originally written (github link).
They meant that they're setting the learning rate multiplier of weights and biases to be 1 and 2 respectively.
Check any prototxt file in the repo e.g. CaffeNet/train.prototxt.
QUESTION
fast rcnn is an algorithm for object detection in images, in which we feed to neural network an image and it output for us a list of objects and its categories within the image based on list of bounding boxes called "ground truth boxes". the algorithm compare the ground truth boxes with the boxes generated by the fast-rcnn algorithm and only keep those that sufficiently overlapped with the gt boxes. the problem here that we must resize the image to be fed into CNN, my question is, should us resize also the ground truth boxes before the comparaison step, and how to do that? tanks to reply.
...ANSWER
Answered 2021-May-31 at 12:20If the bounding boxes are relative, you don't need to change them because 0.2 of the old height is the same as 0.2 of the new height and so on.
QUESTION
2020.06.09
There are 700 images for training, each of them extract 64 rois and make a mini-batch, when batch-size is set to 2, it cast 350 steps to complete training, but for RCNN, each target is extracted as a single image resized to 224*224, there will be 64*700=44800 images, each of which contains more information and features than a 7*7 pooled feature map and I guess that's why it seems under-fitting though RCNN could be train well on same data.
==========================================================================
Use fully balanced data, and acc drops to 0.53 (training data)
...ANSWER
Answered 2020-Jun-09 at 06:06Damn, now I know what problem it is:
In ROI_Pooling.py:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install fast-rcnn
Clone the Fast R-CNN repository
We'll call the directory that you cloned Fast R-CNN into FRCN_ROOT Ignore notes 1 and 2 if you followed step 1 above. Note 1: If you didn't clone Fast R-CNN with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule: git submodule update --init --recursive Note 2: The caffe-fast-rcnn submodule needs to be on the fast-rcnn branch (or equivalent detached state). This will happen automatically if you follow these instructions.
Build the Cython modules cd $FRCN_ROOT/lib make
Build Caffe and pycaffe cd $FRCN_ROOT/caffe-fast-rcnn # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make pycaffe
Download pre-computed Fast R-CNN detectors cd $FRCN_ROOT ./data/scripts/fetch_fast_rcnn_models.sh This will populate the $FRCN_ROOT/data folder with fast_rcnn_models. See data/README.md for details.
Pre-computed selective search boxes can also be downloaded for VOC2007 and VOC2012. This will populate the $FRCN_ROOT/data folder with selective_selective_data.
Pre-trained ImageNet models can be downloaded for the three networks described in the paper: CaffeNet (model S), VGG_CNN_M_1024 (model M), and VGG16 (model L). These models are all available in the Caffe Model Zoo, but are provided here for your convenience.
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