yolact | fully convolutional model for real-time instance | Computer Vision library
kandi X-RAY | yolact Summary
kandi X-RAY | yolact Summary
A simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:. YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's test-dev (check out our journal paper here). In order to use YOLACT++, make sure you compile the DCNv2 code. (See Installation).
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Top functions reviewed by kandi - BETA
- Train the model
- Add elem
- Replaces existing config with new values
- Parse command line arguments
- Log data to disk
- Compute the intersection of two boxes
- Forward computation
- R Compute the difference between two parameters
- Encodes the matched data
- Perform the jaccard search
- Apply the transform
- Render the image
- Compute validation loss
- Optimizes bounding boxes
- Prints the stats
- Computes the intersection of two boxes
- Logs session header
- Plot a single entry
- Encodes a set of matches
- Convert a batch of input images into targets
- Get all supported extensions
- Forward forward computation
- Forward the forward computation
- Parse arguments
- Draw a bar chart
- Forward the convolution
- Add log entries to the database
- Create a layer from configuration
yolact Key Features
yolact Examples and Code Snippets
Lines of Code : 20License : Permissive (Apache-2.0)
Copy@article{gluoncvnlp2019,
title={GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing},
author={Guo, Jian and He, He and He, Tong and Lausen, Leonard and Li, Mu and Lin, Haibin and Shi, Xingjian and Wang, Chenguan 
License : No LicenseCopy# Only CUDA 10.1 Update 1
cd addons
export TF_NEED_CUDA="1"
# Set these if the below defaults are different on your system
export TF_CUDA_VERSION="10.1"
export TF_CUDNN_VERSION="7"
export CUDA_TOOLKIT_PATH="/usr/local/cuda"
export CUDNN_INSTALL_PAT python train.py -tfrecord_train_dir 'path of TFRecord training files'
-tfrecord_val_dir 'path of TFRecord validation files'
-pretrained_checkpoints 'path to pre-trained checkpoints (if any)'
-label_map Community Discussions
Trending Discussions on yolact
QUESTION
I want to train Yolact on a custom dataset using Google Colab+. Is it possible to train on Colab+ or does it time out to easily? Thank you!
...ANSWER
Answered 2022-Feb-14 at 01:38Yes, you can train your model on Colab+. The problem is that Colab has a relatively short lifecycle compared with other cloud platforms such as AWS SageMaker or Google Cloud. I run the code below to extend a bit more such time.
QUESTION
After successfully training my yolact model using a custom dataset I'm happy with the inference results outputted by eval.py using this command from anaconda terminal:
...ANSWER
Answered 2021-Nov-20 at 08:35I will just write the pseudocode here for you.
Step 1: Try loading the model using the lines starting from here and ending here
Step 2: Use this function for evaluation. Instead of cv2.imread, you just need to send your frame
Step 3: Follow this function to get the bounding boxes. Especially this line. Just trackback the 't' variable and you will get your bounding boxes.
Hope it helps. Let me know if you need more clarification.
QUESTION
I am running segmentation on yolact edge. I am trying to find coordinates of the minimu and maximum x and y pixel coordinated of the mask using my own algorithm. I am trying to convert the values of a tuple to numpy. However I am getting the follwoing errror
...ANSWER
Answered 2021-Jul-07 at 12:27Does
QUESTION
I have to build yolact++ in docker enviromment (i'm using sagemaker notebook). Like this
...ANSWER
Answered 2021-May-18 at 09:46You should try setting your CUDA_HOME variable in your dockerfile like this :
QUESTION
Is there any way to save the detected categories, their number, MASK area, etc. to a TXT file or CSV file when performing instance segmentation using YOLACT?
I’m using YOLACT (https://github.com/dbolya/yolact) to challenge instance segmentation. I was able to use eval.py to do an instance segmentation of my own data and save that image or video. However, what I really need is the class names and their numbers detected and classified by YOLACT's AI, and the area of MASK. If we can output this information to a txt file or csv file, we can use YOLACT even more advanced.
If I can achieve that by adding an option in eval.py or modifying the code, please teach me.
Thank you.
...ANSWER
Answered 2020-Nov-11 at 05:18You already have that information from the eval.py.
This line in the eval.py gives you the information.
QUESTION
I have just stuck with Image Instance Segmentation for a while. I am trying to train the Yolact model for my custom data. Here is some brief information about what I have done so far
- I have annotated the image using labelme annotation tool
- I have converted annotation file for each (train & validation data) using labelme2coco -> train.json & test.json
- I made changes in the cofig.py file as needed and expected by yolact
- As I was following this repository I encountered an error Argument 'bb' has incorrect type to which I have solved with the approach stated in this closed issue
After completing the above task I am stuck here with below-stated issue.
...ANSWER
Answered 2020-Nov-04 at 06:58You mispelled your folder names :) YolaDataset needs to be renamed to YolactDataset
QUESTION
I'm new to machine learning and program. Now I'm trying to develop YOLACT AI using my own data. However, when I run train.py, I get the following error and cannot learn. What can I do to overcome this error?`
...ANSWER
Answered 2020-Oct-19 at 09:47Your class id in annotations.json should start from 1 not 0. If they are starting from 0, try this in config.py in your "my_custom_dataset" in label map add this
QUESTION
I am using Yolact https://github.com/dbolya/yolact ,an instance segmentation algorithm which outputs the test image with a mask on the detected object. As the input images are given with the coordinates of polygons around the input classes in the annotations.json, I want to get an output like this. But I can't figure out how to extract the coordinates of those contours/polygons.
As far as I understood from this script https://github.com/dbolya/yolact/blob/master/eval.py the output is list of tensors for detected objects. It contains classes, scores, boxes and mask for evaluated image. The eval.py script returns recognized image with all this information. Recognition is saved in 'preds' in evalimg function (line 595), and post-processing of predict result is in the "def prep_display" (line 135)
Now how do I extract those polygon coordinates and save it in .JSON file or whatever else?
I also tried to look at these but couldn't figure out sadly! https://github.com/dbolya/yolact/issues/286 and https://github.com/dbolya/yolact/issues/256
...ANSWER
Answered 2020-Oct-20 at 09:04You need to create a complete post-processing pipeline that is specific to your task. Here's small pseudocode that could be added to the prep_disply() in eval.py
QUESTION
I want to predict only one class i.e. person from all the 84 classes that are being checked for and predicted.
For YOLACT reference https://github.com/dbolya/yolact
The results are pretty fine but I guess I just need to modify one of the codes and in a very short way but I cant manage to find out
There is one issue related to this in which I did what he mentioned like adding the 4 lines in Yolact/layers/output_utils.py and changing nothing else. Those lines are as following:
...ANSWER
Answered 2020-Sep-29 at 04:51In order to show a single class (person, id:0) output at the time of inference, you simply need to add
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Install yolact
Set up the environment using one of the following methods: Using Anaconda Run conda env create -f environment.yml Manually with pip Set up a Python3 environment (e.g., using virtenv). Install Pytorch 1.0.1 (or higher) and TorchVision. Install some other packages: # Cython needs to be installed before pycocotools pip install cython pip install opencv-python pillow pycocotools matplotlib
If you'd like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into ./data/coco. sh data/scripts/COCO.sh
If you'd like to evaluate YOLACT on test-dev, download test-dev with this script. sh data/scripts/COCO_test.sh
If you want to use YOLACT++, compile deformable convolutional layers (from DCNv2). Make sure you have the latest CUDA toolkit installed from NVidia's Website. cd external/DCNv2 python setup.py build develop
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