darkflow | Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export co | Machine Learning library
kandi X-RAY | darkflow Summary
kandi X-RAY | darkflow Summary
Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2. See demo below or see on this imgur.
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Top functions reviewed by kandi - BETA
- Iterate over the layers and compute configuration .
- Print out the loss
- Capture camera .
- batch image .
- used for testing
- Parse model file .
- postprocess the results
- Parses the command line arguments .
- Make predictions from the input directory .
- Perform training .
darkflow Key Features
darkflow Examples and Code Snippets
Lines of Code : 23License : Permissive (MIT)
Copygit clone https://github.com/AmeyaWagh/Traffic_sign_detection_YOLO.git
cd darkflow
python3 setup.py build_ext --inplace
pip install .
flow --h
cd ..
mkdir dataset
python3 datasetGenerator.py
cd darkflow
mkdir built_graph
{
"yoloConfig":{
"p 
License : No LicenseCopygit clone https://github.com/fernandodelacalle/yolo-darkflow-movidius.git
cd yolo-darkflow-movidius/
pip install --upgrade cython
git clone https://github.com/thtrieu/darkflow darkflow_all
cd darkflow_all
python3 setup.py build_ext --inplace
pip in from darkflow.net.build import TFNet
import cv2
options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.1}
tfnet = TFNet(options)
imgcv = cv2.imread("./sample_img/sample_dog.jpg")
result = tfnet.return_predict(imgcv)
print(r Community Discussions
Trending Discussions on darkflow
QUESTION
I'm a beginner in Machine Learning.
I've been learning about YOLO and DarkFlow from the following links with Ubuntu 20.04: darkflow and Tiny YOLO.
I successfully executed the code, and got the results like this:
Statistics:
car: 436
person: 73
Dataset size: 2599
Dataset of 2599 instance(s)
Training statistics:
Learning rate : 1e-05
Batch size : 16
Epoch number : 1000
Backup every : 2000
It's not bad, but the training's taking way too much time.
But I wanna know if there is any powerful IDE or other tools that can help me reduce time.
I searched in google and tried to find many ways to improve.
I heard that there are many ways to make training faster,(including Azure AI ML service) but since I'm a beginner, I cannot decide which will be the perfect choice to run YOLO and DarkFlow.
I would appreciate advices about robust development environments, especially the ones that would be suitable in my current specific condition.
Thanks in advance!
++) Since I'm a mere sophomore, the level of the hardware that I can use is very limited. I would also appreciate tools that can help me overcome the limitations of my hardware!
...ANSWER
Answered 2021-Apr-04 at 20:39The IDE usually won't decrease computing time, but is rather based around the compiler. Likely, you'll just have to deal with intensive training times if you can't get better hardware. However, you might look into using a gpu to do computations(if you have one) rather than the standard way it runs on the cpu. Here's a link as to how to achieve this in Tensorflow: https://stackoverflow.com/a/51307381/14392018 . The general rule is the more data and the more complex the ML/DL model(i.e. the more layers in a neural network), the longer it takes to compute 1 epoch on the dataset. Also, the more data you're working with, the higher the computational intensity.
QUESTION
I've been studying darkflow from the following link;
https://github.com/thtrieu/darkflow
on Ubuntu 20.04
I thought there was a problem in my flow file, so I tried to rebuild the build file by the following code
ANSWER
Answered 2021-Apr-04 at 10:58You can try :
QUESTION
I'm trying to take the data stored in symbol and have puppeteer type it into a search bar using scrapePage.type. I am not receiving any error messages but the code is acting as if data.symbol is not holding any value even though it is because when it sends the embed message, the .addField for symbol use data.symbol to send the message.
ANSWER
Answered 2020-Jun-05 at 07:01data is an array of objects. When you use each object in a loop like in for (let entry of data) { ... addField('Contract Selection', entry.symbol), the symbol is there. But data array has no .symbol property outside of a loop, it is undefined. It has to be something like data[0].symbol.
QUESTION
I have 2 Tensorflow Lite models (they are Yolo V2 Tiny models):
- Model A) Downloaded from the internet, detects and classifies objects with 80 classes. The .tflite files weights 44,9mb.
- Model B) Trained by myself using Darknet, detects and classifies objects with 52 classes. The .tflite files weights 20,8mb. The model is converted to TFLite using Darkflow.
However both on a mobile phone and on a computer model B takes 10x more time to predict than model A (even if model B detects within less classes and its file is lighter). Also, models seem to work with input images of size 416x416 and use float numbers.
What could be the reason for model A being faster than model B? How can I find out why model A is faster?
One of the problems I have is that for model A, since I have not trained it myself, I don't have its .cfg file with the whole setup...
...ANSWER
Answered 2020-Mar-16 at 17:11You should try the following two approaches to gain more insight, as the reasons to why a model happens to be slower than expected could be several.
Inspect both networks with a tool like Netron. You can upload your flatbuffer (TF Lite) model file and visualize the network architecture after TF Lite conversion. There you can see where the difference between the two models lies. If e.g. there happen to be additional Reshape operations or alike in Model B compared to A, that could likely be a reason. To download Netron follow https://github.com/lutzroeder/netron.
Measure the time spent by the model on each of its layers. For this you can use the TF Lite benchmark tool provided directly in the Tensorflow repository. Check it out here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/benchmark/README.md.
QUESTION
Hello there,
I am trying to use DarkFlow, a Python implementation of YOLO (which uses Tensorflow as backend), on my Nvidia Jetson Nano to detect objects. I got all the setup and stuff, but it doesn't want to train. I set it to GPU mode and a line in the output says this:
...ANSWER
Answered 2020-Mar-08 at 09:19You may try to decrease batch_size to 1 and lower the width,height values but would not recommend a training session on jetson nano. Its limited capabilities(4 GB shared RAM) hinders the learning process. To counter the limitations you could try to follow this post or this one to increase swap_area which acts as RAM but still I would recommend using nano only for inference.
EDIT1: Also it is known that Tensorflow has a tendency to try to allocate all available RAM which makes the process killed by OS. To solve the issue you could use tf.GPUOptions to limit Tensorflow's RAM usage.
Example:
QUESTION
I'm training a model to use for object detection using YOLO-darkflow but i get this error. How can i solve it.
...ANSWER
Answered 2020-Jan-30 at 10:29you shouldn't pass the annotations into the training parameter, try reformating the command to look more like this:
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Install darkflow
Just build the Cython extensions in place. NOTE: If installing this way you will have to use ./flow in the cloned darkflow directory instead of flow as darkflow is not installed globally. python3 setup.py build_ext --inplace
Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect) pip install -e .
Install with pip globally pip install .
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