gluon-cv | | Installation | Documentation | Tutorials | Machine Learning library
kandi X-RAY | gluon-cv Summary
kandi X-RAY | gluon-cv Summary
| Installation | Documentation | Tutorials |. GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
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Top functions reviewed by kandi - BETA
- Train the Gluon network .
- Load a COCO .
- Export the given block to TVM .
- Compute the direct loss for each class .
- Train image classification .
- Overlay instances .
- Convert dataset dict to COCO format .
- Autocomplete .
- Fit the model .
- Export a block of data
gluon-cv Key Features
gluon-cv Examples and Code Snippets
Lines of Code : 156License : Non-SPDX (Apache License 2.0)
Copy"""2. Train Mask RCNN end-to-end on MS COCO
===========================================
This tutorial goes through the steps for training a Mask R-CNN [He17]_ instance segmentation model
provided by GluonCV.
Mask R-CNN is an extension to the Faster """06. Train Faster-RCNN end-to-end on PASCAL VOC
================================================
This tutorial goes through the basic steps of training a Faster-RCNN [Ren15]_ object detection model
provided by GluonCV.
Specifically, we show how t """04. Train SSD on Pascal VOC dataset
======================================
This tutorial goes through the basic building blocks of object detection
provided by GluonCV.
Specifically, we show how to build a state-of-the-art Single Shot Multibox
De def find_class_idx(label):
"""
Should return the class index of a particular label.
:param label: label of class
:type label: str
:return: class index
:rtype: int
"""
return network.classes.index(label)
# map class ID to classes
id2string = [i:name for i, name in enumerate(net.classes)]
# filter on score.
thresh = 0.8
top_classIDs = [c for c, s in zip(box_ids[0], scores[0]) if s > thresh]
# convert IDs to class names into "label1"
lapip install --upgrade 'setuptools<45.0.0'
rgb_nd = rgb_nd.as_in_context(ctx)
class_IDs, scores, bounding_boxes = net(rgb_nd)
class_IDs, scores, bounding_boxes = net(rgb_nd)
if isinstance(class_IDs, mx.ndarray.ndarray.NDArray):
class_IDs.waifrom matplotlib import pyplot as plt
from gluoncv import model_zoo, data, utils
from gluoncv.data.transforms.pose import detector_to_alpha_pose, heatmap_to_coord_alpha_pose
detector = model_zoo.get_model('yolo3_mobilenet1.0_coco', pretrainet.set_nms(nms_thresh=0.5, nms_topk=50)
#!/usr/bin/python3
# 2019/01/24 09:05
# 2019/01/24 10:25
import gluoncv as gcv
import mxnet as mx
import cv2
import numpy as np
# https://github.com/pjreddie/darknet/blob/master/data/dog.jpg
## (1) Create network
net = gcv.model_zoo.getCommunity Discussions
Trending Discussions on gluon-cv
QUESTION
I used GluonCV to finetune an object detection model in order to recognize some custom classes, mostly following the related tutorial.
I tried using both “ssd_512_resnet50_v1_coco” and “ssd_512_mobilenet1.0_coco” as base models, and the training process ended successfully (the accuracy on the validation dataset is reasonably high).
The problem is, I tried running inference with the newly trained model, by using for example:
...ANSWER
Answered 2020-Aug-24 at 13:46Ok, fixed it. Basically, during training I was saving the .params file by using:
QUESTION
Similar to the issue of The trained model can be deployed on the other platform without dependency of sagemaker or aws service?.
I have trained a model on AWS SageMaker by using the built-in algorithm Semantic Segmentation. This trained model named as model.tar.gz is stored on S3. So I want to download this file from S3 and then use it to make inference on my local PC without using AWS SageMaker anymore. Since the built-in algorithm Semantic Segmentation is built using the MXNet Gluon framework and the Gluon CV toolkit, so I try to refer the documentation of mxnet and gluon-cv to make inference on local PC.
It's easy to download this file from S3, and then I unzip this file to get three files:
- hyperparams.json: includes the parameters for network architecture, data inputs, and training. Refer to Semantic Segmentation Hyperparameters.
- model_algo-1
- model_best.params
Both model_algo-1 and model_best.params are the trained models, and I think it's the output from net.save_parameters (Refer to Train the neural network). I can also load them with the function mxnet.ndarray.load.
Refer to Predict with a pre-trained model. I found there are two necessary things:
- Reconstruct the network for making inference.
- Load the trained parameters.
As for reconstructing the network for making inference, since I have used PSPNet from training, so I can use the class gluoncv.model_zoo.PSPNet to reconstruct the network. And I know how to use some services of AWS SageMaker, for example batch transform jobs, to make inference. I want to reproduce it on my local PC. If I use the class gluoncv.model_zoo.PSPNet to reconstruct the network, I can't make sure whether the parameters for this network are same those used on AWS SageMaker while making inference. Because I can't see the image 501404015308.dkr.ecr.ap-northeast-1.amazonaws.com/semantic-segmentation:latest in detail.
As for loading the trained parameters, I can use the load_parameters. But as for model_algo-1 and model_best.params, I don't know which one I should use.
ANSWER
Answered 2020-Mar-02 at 05:15The following code works well for me.
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Install gluon-cv
GluonCV supports Python 3.6 or later. The easiest way to install is via pip.
GluonCV supports Python 3.6 or later. The easiest way to install is via pip.
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