I have trying to develop machine learning based image classification system using Scikit-Learn. I am trying to do is multi class classification. the biggest problem i am facing with Scikit-Learn is how to load the data. Then I came across one of the examples face_recognition.py. which using fetch_lfw_people to fetch data from internet. I could see this example actually does multi class classification. I was trying to find some documentation on the example but was unable to find. I have some question here, what does fetch_lfw_people do ? what does this function load in the lfw_people. Also what i saw in the data folder there are some text file .is the code reading the text files/? My main intention is to load my set of image data but i am unable to do it with fetch_lfw_people in case i change the path that my image folder by data_home and funneled=False.I get erros, I hope i get some answers here
First thing first. You can't directly give images as an input to your classifier. You have to extract some features from you images. Or you can load your image using opencv and use the numpy array as an input to your classifier.
I would suggest you to read some basics of image classification , like how you can train your classifier and all.
Coming to your question about fetch_lfw_people function. It might be downloading already pre-processed image data from any text file. If you are training from your images you have to first convert your image data to some numerical features.
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everyone.
I am trying to create a CNN which can upon being fed input of images classify which part of the image to focus upon. For that purpose, I have collected data by obtaining gaze data of humans for a given video and divided each video frame into 9 different areas. With the actual gaze data acting as the supervisory data, I am trying to make my system learn how to mimic a human's eye gaze.
For starters, I am using a pre-built CNN for the classification of the MNIST dataset using tensorflow. I am currently trying to make my dataset follow the format of MNIST dataset keras.datasets.mnist. I have video frames in .jpg format and the corresponding grid area as a NumPy array.
I am stuck on how to correctly label and format my images so that I can directly feed the image into the pre-built CNN. System I am using tensorflow 2.7.0, python 3.9.7 using conda.
Any help is very appreciated.
I’m currently working on object detection using yolov5. I trained a model with a custom dataset which has 3 classes = [‘Car’,‘Motorcycle’,‘Person’]
I have many questions related to yolov5.
All the custom images are labelled using Roboflow.
question1 : As you can see from the table that my dataset has mix of images with different sizes. Will this be a problem in training? And also assume that i’ve trained the model and got ‘best.pt’. Will that model work efficiently in any dimensions of images/videos.
question 2:
Is this directory model correct for training. Even i have ‘test’ directory but it seems that the directory is not at all used. The images in the ‘test’ folder is useless. ( I know that i’m asking dumb questions, please bare with me.)
Is it ok if place all my images like this
And should i need a ‘test’ folder?
question3: What is the ‘imgsz’ in detect.py? Is it downsampling the input source?
I’ve spent more than 3 weeks in yolo. I love it but i find some parts difficult to grasp. kindly provide suggestion for this questions. Thanks in advance.
"question1 : As you can see from the table that my dataset has mix of images with different sizes. Will this be a problem in training? And also assume that i’ve trained the model and got ‘best.pt’. Will that model work efficiently in any dimensions of images/videos."
As long as you've resized/normalized all of your images to be the same square size, then you should be fine. YOLO trains on square images. You can use a platform like Roboflow to process your images so they not only come out in the right structure (for your images and annotation files) but also resize them while generating your dataset so they are all the same size. http://roboflow.com/ - you just need to make a public workspace to upload your images to and you can use the platform free. Here's a video that covers custom training with YOLOv5: https://www.youtube.com/watch?v=x0ThXHbtqCQ
Roboflow's python package can also be used to extract your images programmatically: https://docs.roboflow.com/python
"Is this directory model correct for training. Even i have ‘test’ directory but it seems that the directory is not at all used. The images in the ‘test’ folder is useless. ( I know that i’m asking dumb questions, please bare with me.)"
Yes that directory model is correct from training. Its what I have whenever I run YOLOv5 training too.
You do need a test folder if you want to run inference against the test folder images to learn more about your model's performance.
The 'imgsz' parameter in detect.py is for setting the height/width of the images for inference. You set it at the value you used for --img when you ran train.py.
For example: Resized images to 640 by 640 when generating your images for training? Use (640, 640) for the 'imgsz' parameter (that is the default value). And that would also mean you set --img to 640 when you ran train.py
detect.py parameters (YOLOv5 Github repo)
train.py parameters (YOLOv5 Github repo)
YOLOv5's Github: Tips for Best Training Results https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results
Roboflow's Model Production Tips: https://docs.roboflow.com/model-tips
I was working on a face recognition code. I wanted to find the performance metrics (accuracy, precision, etc.) of my algorithm. I've used HaarCascade Algorithm and LBPH FaceRecogniser When I searched for it on the net, I could find only those sources where already existing datasets are taken and the parameters are computed. I want to use the data obtained from training my model (trained from the images folder). A folder named "trainner.yml" is generated automatically after running the file.
is the data from the trainner.yml file my dataset? What is my dataset now and how can I find the confusion matrix
Thanks
I want to train a LayoutLM through huggingface transformer, however I need help in creating the training data for LayoutLM from my pdf documents.
Multi page Document Classification can be effectively done by SequenceClassifiers. So here, is a strategy:
Convert Your PDF pages into images and make directory for each different category.
Iterate through all images and create a csv with image Path and label.
Then define your important features and encode the dataset.
Save it in your disk.
Load it back when you need it using Load_from_disk and dataloader.
Good morning,
I have been working with the tensorflow object detection tutorial using the ssd_mobilenet they are providing as a frozen graph as well as with the corresponding checkpoint files (model.ckpt.data-00000-of-00001, model.ckpt.index, model.ckpt.meta).
However, as the images are sometimes badly recognized, I hoped I could feed own images to the detection model and improve its performance for my images, that are all taken by the same camera.
Google could not help me where to start. The questions I am having:
- Are there any code snippets that show which of those files to load and how to train the existing model?
- Do I need to retrain the loaded model with the old data (i.e. COCO) + the new data (my images) or can I just retrain it using my data and the model remembers what it has learned before?
Sorry for this very unspecific questions, but I just can not figure out where to start.
There is a great walkthrough blog and code base written by Dat Tran. He trained a model to recognize Raccoons in images using the pre-trained SSD_mobilenet as a start. This is the best place I found to start. Hope this helps.