kandi X-RAY | detectron2 Summary
kandi X-RAY | detectron2 Summary
Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms. It is the successor of Detectron and maskrcnn-benchmark. It supports a number of computer vision research projects and production applications in Facebook.
Top functions reviewed by kandi - BETA
- Generate instance class .
- Convert files to a dictionary .
- Collect environment information .
- Evaluate an image
- Overlay instances .
- Rename the Caffe2 weights .
- Convert datasets to COCO format .
- Aligns two state dictionaries .
- r Evaluate the vocab .
- Finds the top - N proposals for the given proposals .
detectron2 Key Features
detectron2 Examples and Code Snippets
|--- | |---annotations | | |---EPIC_test_s1_object_video_list.csv | | |---EPIC_test_s2_object_video_list.csv | | |---EPIC_train_object_labels.csv | | |---. | | |---. | |---EPIC_KITCHENS_2018 | | |---object_detection_images
./d2_on_sagemaker/ ├── README.md ├── requirements.txt ├── d2_eval.ipynb ├── d2_inference.ipynb ├── d2_local.ipynb ├── d2_train.ipynb ├── dataprep │ ├── SMGT_to_COCO.py │ ├── dataprep.ipynb │ └── go_smgt.ipynb ├── s
# the import automatically registers all datasets to d2 from nicr_scene_analysis_datasets import d2 as nicr_d2 # set the path for the dataset, so that d2 can use it # note, dataset_path must point to the actual dataset (e.g. ../datasets/hypersim) #
# Copyright (c) Facebook, Inc. and its affiliates. import argparse import glob import multiprocessing as mp import numpy as np import os import tempfile import time import warnings import cv2 import tqdm from detectron2.config import get_cfg from de
# Copyright (c) Facebook, Inc. and its affiliates. import atexit import bisect import multiprocessing as mp from collections import deque import cv2 import torch from detectron2.data import MetadataCatalog from detectron2.engine.defaults import Defa
Trending Discussions on detectron2
I have trained the model, now i would like to use it to detect objects in many images. I saw that the defaultpredictor allows you to detect only on an image, what can I do?
I am really new to this world. The approach I tried was to use a for loop but it doesn't work. Are there any other methods?...
ANSWERAnswered 2022-Apr-09 at 09:45
Ok, i solved in this way:
I finished training model for instance segmentation in detectron2 when I test images in training files there is no problem class names(apple,banana,orange) are written on the image but I downloaded some fruit images from the internet and class names are not written on the photos. There are class ID's....
ANSWERAnswered 2022-Apr-02 at 13:36
You can get the labels by populating the
metadata kwarg, which contains the mapping.
I'm trying to train a custom COCO-format dataset with Detectron2 on PyTorch. My datasets are json files with the aforementioned COCO-format, with each item in the "annotations" section looking like this:
The code for setting up Detectron2 and registering the training & validation datasets are as follows:...
ANSWERAnswered 2022-Mar-29 at 11:17
It's difficult to give a concrete answer without looking at the full annotation file, but a
KeyError exception is raised when trying to access a key that is not in a dictionary. From the error message you've posted, this key seems to be
This is not in your code snippet, but before even getting into network training, have you done any exploration/inspections using the registered datasets? Doing some basic exploration or inspections would expose any problems with your dataset so you can fix them early in your development process (as opposed to letting the trainer catch them, in which case the error messages could get long and confounding).
In any case, for your specific issue, you can take the registered training dataset and check if all annotations have the
'segmentation' field. A simple code snippet to do this below.
I've trained a mask r-cnn on corn images (I cannot show examples because they are confidential), but they are basically pictures of corn kernels scattered over a flat surface.
There are different kinds of corn kernels I want to be able to segment and classify. I understand the AP metrics are the best way of measuring the performance of an instance segmentation algorithm and I know a confusion matrix for this kind of algorithm doesn't usually make sense.
But for his specific case, where I have 4 classes of very similar objects, I would like to be able to set a fixed AP value, like AP50/AP75 and build a confusion matrix for that.
Would it be possible? How would I do it?
I used detectron2 library to train and get predictions. Here is the code I use to load my trained model from disk, generate predictions in the validation set, and visualize the results:...
ANSWERAnswered 2022-Mar-18 at 16:54
I was able to do it, I built the confusion matrix function from scratch:
I trained a custom model with detectron2 on
google colab, and ok, it's working correctly. The model was trained, the predictions were ok, this on
google colab. But when I made predictions on my local machine did'nt work. Here a similar example on google colab: https://colab.research.google.com/drive/1bSlH5Am_zFEWbJ9zTRu2wFEDKDvn0LUv?usp=sharing
I exported de final model and ran with this code:...
ANSWERAnswered 2022-Mar-04 at 16:25
I saved the configs with this comand and then I downloaded.
- I have a detectron2 Mask R-CNN baseline model that is good enough to predict some object boundaries accurately.
- I'd like to convert these predicted boundaries to COCO polygons to annotate the next dataset (supervised labeling).
- To do this, I need to run inference on an image dataset that does not have annotations.
- The detectron2 methods
load_coco_jsonrequire images with annotations to properly label the predicted objects.
ANSWERAnswered 2021-Dec-02 at 14:39
Here's a method to generate the image details from a directory of images and write it to an existing COCO JSON file:
Despite changing the classes line to...
ANSWERAnswered 2022-Feb-15 at 10:42
I figured it out and it was me being dumb
So let my dumbness provide an answer for anyone else stuck up this particular creek.
So in addition to adding
I am just trying out detectron2 with some basic code as follows...
ANSWERAnswered 2022-Feb-03 at 18:03
I had the same issue, for me I had two issues to fix. The first was resizing shortest edge. I used the Detectron2 built function from detectron2.data.transforms and imported ResizeShortestEdge. The model values can be found with cfg.INPUT, which will list max/min sizes for test and train. The other issue was matching the color channels with cfg.
I am training a model on Faster R CNN architecture. For the first session I used the below config:...
ANSWERAnswered 2021-Dec-06 at 16:19
There is no error actually.
The problem is that your config specifies the maximum iteration as 16000.
In the balloon.py file in Detectron2 samples, I get a KeyError of 'regions' whenever I run the balloon.py on my custom dataset. I figured something was wrong with the json file in the train folder, so I first used the latest VIA 3 and then VIA 2.0.0. Both jsons create the same KeyError.
I compared the balloon's training VIA json to my training VIA json, and they have the same structure now, so I'm thinking it isn't a json issue anymore. Why would Python not be able to read a string as a key?
ANSWERAnswered 2021-Dec-05 at 21:27
You haven’t given us your JSON so it’s impossible to tell really, but scanning over the file in the link I don’t think this is you fault, line 117 of balloons.py is
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