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ultimate teaching style, this was the best computer vision introduction tutorial I have ever seen on youtube as it was very easy to understand , thanks a lot Philip!!
Amazing job Felipe, you are helping me so much on my studies! I'm from Brazil and I will definitely use your videos to help on my completion of course work
Thanks a lot for your content Felipe, you're a great teacher! I'm new to machine learning so there's one thing that confuses me: how can I now utilize the the model that I've created? I understand that Pickle transforms the data into byte form and that it can be read somehow, but could you give me an example of what to now do with this data?
Sure! Take a look at this function, here is an example of how I use the same image classifier I trained in this video to make predictions github.com/computervisioneng/parking-space-counter/blob/master/util.py#L14 Please mind I am loading the model in line 11. 💪💪
Thank you very much, Felipe, for these very clear explanations. Until now, I was making predictions for numerical values. Before watching your videos, I thought image classification was more complicated (even though this is a relatively straightforward use case). I have just one perhaps silly question: How did you create the image captures for the dataset? Was it done manually, or did you use specific software for this somewhat tedious task? Thanks again!
Hi, I took the parking lot video I show in the tutorial, I created a binary mask with the location of all the parking slots, then got all the connected components using opencv, then cropped the bboxes from the video frames. 🙌
Could you release the data of empty and not empty for us? Thank you very much for your amazing tutorials on OpenCV and Machine Learning. It is beneficial for me to construct my own project!
Sure! I am happy to help you in your machine learning journey! 😃🙌 Here you go: drive.google.com/file/d/11DyZ165lZGzULEZSQNofyy9A8xaYgFJ2/view?usp=share_link
Thank you for a great clear video. I am going to run it through. Yet something is missing for me: I would like to train a set of specific toy car recognition. For that I need to take pictures of the toy car , and pictures of the floor/table (where the car can be placed) for the "empty' part. Can you recommend: 1) how many pictures of each? 2) What resolution? does it matter? 3) Any other guide line you would recommend?
About the number of pictures, you should be able to achieve a high level of accuracy with a 'small' dataset; try with 200 images in each class. The resolution may not be that critical, but try to make it as high as possible. 🙌
Thumbs up for this video. I just noticed a small mistake . I think you accidentally switched the parameters for the accuracy_score function. It should be accuracy_score(y_test, y_prediction) instead of accuracy_score(y_prediction, y_test).
Hey, thank you for your contribution! Although the order of input parameters is not crucial at computing the accuracy, the function outputs the same result 💪🙌
Great video. Is there a follow up on this to show how you can use the classifier and display the annotated image with green box and red box representing occupied and unoccupied spots in a parking lot image?
Hi, please I have a project that is similar to yours and I would please like your assistance in some questions. The label being passed into the training and testing set, is it numerical or in string format? Cause I have to use labelencoder for my label before passing it into the function. Then the data being saved in the data variable is there any way I can convert them into a 4D array to be able to pass it into a CNN model? Thank you.
Hey, the label is numerical. Sure, you could reshape 'data' into a 4D array, it is possible. In that case, you may don't need to flatten the array as I did. 😃🙌
amazing tutorial, thanks! I had a question about the model.p file, is there some way I can open it and view its contents, I want to know how it classified, or maybe to see if the task was done as I wanted it to be done.
For models trained with scikit learn, in some cases is possible to look at the model weights or some of its internal parameters. But in general I would say model.p is a binary file and it only makes sense in scikit learn, there is not an easy way to 'open it and view its content'. 🙌
Hi, great tutorial! I seem to be stuck on a problem when I execute the "grid_search.fit(x_train, y_train)" part of the code. This is the issue "FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: TypeError: only size-1 arrays can be converted to Python scalars". Do you know how to fix this? I have ensured to use the correct versions (scikit learn, scikit image, and numpy) as shown in video, and followed the video step by step. This is the code for importing images i have (i had to add dtype=object when converting lists to numpy array as it was giving me visibleDeprecationWarning): for category_idx, category in enumerate(categories): for file in os.listdir(os.path.join(input_dir, category)): img_path = os.path.join(input_dir, category, file) img = imread(img_path) print(len(data)) img = resize(img, (200, 200)) data.append(img.flatten()) labels.append(category_idx) data = np.asarray(data, dtype=object) labels = np.asarray(labels, dtype=object). Finally, do you have an instagram or other site to discuss this issue, if possible?
Hey, what is your Python version and your OS? Many things could be going on. I would start by making sure the data and labels are being loaded correctly. You could print 'data' and 'labels' and see if they make sense.
i need your advice i have task where i need to find avarage pixal value of x-ray image object in x-ray image i have many object like pen paper palstic and so on and each intesity is different some are overlap some have no clear edegs and mixed edges can i detect or draw a boundry box around each object is it possible without ml model i am newbew in this field specially in image processing can it can be done by image process alone can i segement object if intesity of each object is different and edges are not so clear please help me i am single develpoer in my team
Hi Felipe, Actually I wanted to ask that is this model build using pretrained model or not? and If it is build on pre trained model can you suggest me a video of yours where I can learn building a model with using a pretrained model
also i wanted to know about your video named as sign language detection with scikit learn. The thing is in that video the sign detection one of yours are you using any pre trained model?
@@ComputerVisionEngineer hey felipe i loved the tutorial but if i wanna see how the model recognises the parking spaces in a video and its real time working ? actually i wanted to see the output of it
I have a task to classify cat and dog so i tried the same way you do it but the accuracy its very low i used gray scale to minimize the complicity but didont work , i want to used like this approach if you has any advice
@@odaithalji9603 by increasing the size in line 23 performance should go up. Nevertheless, this may not be the best approach for this type of task. I would recommend you to use feature extraction before applying a scikit learn classifier. I may do a video about it soon. 💪
Could you make another video when you test out this classifier?. Like host this online and let the user upload images of parking spaces and the program outputs 'empty' or 'not empty'. Thanks
Hey, oh I see the inference of individual images is not covered in this tutorial. Take a look at this video czcams.com/video/MJCSjXepaAM/video.html, in this video I also train a classifier using scikit learn and I show you how to inference new data. 🙌
hi, qq how did you split the images to empty and not empty? I saw the other video where you cropped images, but how did you separate 6K+ images into empty and not empty, its not clear.
The opencv function I used to get all connected components provides an id for every component, so I looked at the ids of empty slots and non empty slots and save them into different directories.
@@ComputerVisionEngineer i see, thank you. Yes I also looked at the other videos you have and realized how its done. Thanks for responding. Btw I pinged you on linedin for a project. I did not see reply, assuming you are not logging in to that often. Would you be interested in a paid consultation?
@@user-vn1wy8qc3k thank you so much for reaching out regarding a paid consultation, I do work as a freelance contractor, but I am a little too busy right now with other projects. Yeah I don't use linkedin that much, but you can reach out to me through mail, discord, Patreon, or the comments in my videos, I try to answer as soon as possible. 🙌
Sir, I have facing some error while training the classifier. It says : ValueError: Found input variables with inconsistent numbers of samples: [4872, 1218] It would be of great help if you solve this problem🙏
@@ComputerVisionEngineer sounds great, trying to come up with a plan to computer vision something either its a good object or a bad one depending on some criteria
If I have more that 2 categories of images, can I name “x_train” and “x_test” whatever I want? For example “car_train”, “car_test”, “bus_train”, “bus_test”, and “limo_train”, “limo_test”?
Hey, you can certainly name your variables whatever you want. But, remember the category is already encoded in the labels variable. For example if you are building a vehicle classifier and the categories are car, bus and limo. Then each data point will be labeled as 0 (car), 1(bus) or 2 (limo).
Hey, that sounds like a cool project! As long as the different classes are visually distinct, you can classify them with an image classifier! I suggest you start with the data collection; collect data from all the different categories you wish to classify. Once you have the data, the Python code would be very similar as the one I used in this video! 😃💪🦆
How do you build up your trainig data? You won‘t have decided for 6000 pictures whether they are empty or not? Did you use kind of a „step by step“ classification? So that with lets say with 50 pictures you built up a classifier, that builds up further (and of course checked it then whether it worked properly? Thanks for your videos!
Hey Niklas! That is a good question. The data I used in this video comes from a previous video where I built a parking space detector and counter. In that other video I got the location of all parking slots by getting all the connected components of a binary mask. The function I used for getting the connected components got me not only the bbox but also an id for that specific location. Then, as most of the spaces are either empty or not-empty during the entire video, I just selected a few empty spaces (and a few not-empty spaces) and cropped them out of the video in all frames. 💪💪
When i was trying to convert data and labels into numpy array, i got this error😢 ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (84,) + inhomogeneous part. What is this ? Thanks for the amazing tutorial ❤
@@ComputerVisionEngineer No sir, first i used a data set that i have created, when i load your data set, now it is working 😍 But I want to know what is that error ? How it occurred 🥲
Hello Sir, i used the same code, but its not giving me the output result, like don't print the accuracy... kindly help! thats what it is showing in the notification area. Shared indexes are downloaded for Python packages in 1 min, 12 sec (20.48 MB)
Hey, I just tested the code and everything seems to be working well on my end! That msg seems to be related to Pycharm, maybe you could try to execute from another IDE or from the command line?
Ciao Fillippe, I have question about this profession in 2024 and want to know your opinion. Do I need to study any course in online platforms to become a good on it and of course finde a job(take certificate for CV). And is it easy to find a job as Comp.Vision engineer nowadays(2024 and maybe future). What can you advise me to become an engineer as you (online courses, books ect...) P.S. I really appreciate your contents, thank you for your time and effort. Hope to become a professional com.vision engineer and send you a gift from my 1st salary(but now i dont know, how to become it)
Hi, take a look at the computer vision roadmap video, there are links to youtube videos and resources you can use to learn computer vision engineering. 🙌
Hey Endah, there are different ways in which you can deploy the model we created in Scikit learn in an Android app. If you are looking to run the model in the mobile device you could consider the Python library sklearn2onnx in order to convert the Scikit learn model into an onnx file. 😃💪
@@ComputerVisionEngineer do you have any reference to change the model? I had trouble finding the reference, I used this classification method for my thesis and my deadline is only 2 days to be able to run this program. I hope you can help me, thank you
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you're the best Felipe! short videos straight to the point, great explanations
Thank you for your support! 😃💪
ultimate teaching style, this was the best computer vision introduction tutorial I have ever seen on youtube as it was very easy to understand , thanks a lot Philip!!
You are welcome! Thank you for your support! 😃🙌
Thank you, very clear and easy to follow tutorial
The way you explained this topic is amazing, I enjoyed watching the video and I didn't even feel the length of it.
Hey Imad, thank you! I am so glad you enjoyed it! 😄💪
AWESOME! Made my first working ML model. Thank you so much!!!!
😃 That is so cool!! You are welcome! 🙌
Amazing job Felipe, you are helping me so much on my studies! I'm from Brazil and I will definitely use your videos to help on my completion of course work
Hey Guilherme! It makes me so happy the videos are helping you! 😃😃
Thank you for watching! 🤓💪
I love the explanation and you helped me so much!
You are welcome! Glad you found it helpful! 😃🙌
Thank you so much for your time, you did a great job.
Thank you for your support, Hamzawi! 🙂🙌
Thank you so much for this Video. Makes fun and have learning effect.
😃 Glad you enjoyed it! 🙌
Your tutorial is very cool, thank you very much for helping me with your tutorial
😊 You are welcome! 🙌
Felipe the Legend 👏🏽
lol, thank you for your support! 🙌
Very helpful ❤
😊 Thank you, Abishek! I am glad you find it useful! 🙌
Great video!
Thank you! 😃🙌
It helped a lot for me.
Awesome! I am happy you found it helpful! 😄🙌
Thanks a lot for your content Felipe, you're a great teacher! I'm new to machine learning so there's one thing that confuses me: how can I now utilize the the model that I've created? I understand that Pickle transforms the data into byte form and that it can be read somehow, but could you give me an example of what to now do with this data?
Sure! Take a look at this function, here is an example of how I use the same image classifier I trained in this video to make predictions github.com/computervisioneng/parking-space-counter/blob/master/util.py#L14
Please mind I am loading the model in line 11. 💪💪
Thank you!
Thank you very much, Felipe, for these very clear explanations. Until now, I was making predictions for numerical values. Before watching your videos, I thought image classification was more complicated (even though this is a relatively straightforward use case). I have just one perhaps silly question: How did you create the image captures for the dataset? Was it done manually, or did you use specific software for this somewhat tedious task? Thanks again!
Hi, I took the parking lot video I show in the tutorial, I created a binary mask with the location of all the parking slots, then got all the connected components using opencv, then cropped the bboxes from the video frames. 🙌
Could you release the data of empty and not empty for us? Thank you very much for your amazing tutorials on OpenCV and Machine Learning. It is beneficial for me to construct my own project!
Sure! I am happy to help you in your machine learning journey! 😃🙌
Here you go: drive.google.com/file/d/11DyZ165lZGzULEZSQNofyy9A8xaYgFJ2/view?usp=share_link
amazing tutorial indeed! ❤️ 👌 can you suggest me some resources where i can find some free data for training models?
Amazing tutorial !!, Just a question, there are many classifiers in sklearn. Why did you choose SVC specifically ?
No reason. Given the data I used in this project, I think pretty much any other classifier would have had a similar performance. 💪💪
Thank you for a great clear video. I am going to run it through. Yet something is missing for me: I would like to train a set of specific toy car recognition. For that I need to take pictures of the toy car , and pictures of the floor/table (where the car can be placed) for the "empty' part. Can you recommend: 1) how many pictures of each? 2) What resolution? does it matter? 3) Any other guide line you would recommend?
About the number of pictures, you should be able to achieve a high level of accuracy with a 'small' dataset; try with 200 images in each class. The resolution may not be that critical, but try to make it as high as possible. 🙌
A great session by you sir , sir pls also share the code to input a photo in it and put it in the trained model to show the results of analysation
Do you mean the code to make predictions on individual images? I will try to add that code soon.
Thumbs up for this video. I just noticed a small mistake . I think you accidentally switched the parameters for the accuracy_score function. It should be accuracy_score(y_test, y_prediction) instead of accuracy_score(y_prediction, y_test).
Hey, thank you for your contribution! Although the order of input parameters is not crucial at computing the accuracy, the function outputs the same result 💪🙌
Great video. Is there a follow up on this to show how you can use the classifier and display the annotated image with green box and red box representing occupied and unoccupied spots in a parking lot image?
Thank you! Sure, visualization is covered in the parking spot detector and counter video! czcams.com/video/F-884J2mnOY/video.html
Hi, please I have a project that is similar to yours and I would please like your assistance in some questions.
The label being passed into the training and testing set, is it numerical or in string format? Cause I have to use labelencoder for my label before passing it into the function.
Then the data being saved in the data variable is there any way I can convert them into a 4D array to be able to pass it into a CNN model?
Thank you.
Hey, the label is numerical. Sure, you could reshape 'data' into a 4D array, it is possible. In that case, you may don't need to flatten the array as I did. 😃🙌
This is really anazing.can you make a video on how to apply GAN and use it or make it from scartch?
Hey Rakesh, making a video about GANs is a very good idea! I will definitely keep it in mind for future videos! 😄
amazing tutorial, thanks!
I had a question about the model.p file, is there some way I can open it and view its contents, I want to know how it classified, or maybe to see if the task was done as I wanted it to be done.
For models trained with scikit learn, in some cases is possible to look at the model weights or some of its internal parameters. But in general I would say model.p is a binary file and it only makes sense in scikit learn, there is not an easy way to 'open it and view its content'. 🙌
thank you felipe
You are welcome! 😃
You are use category_index for labels ?? I think the labels is category
Hi, great tutorial! I seem to be stuck on a problem when I execute the "grid_search.fit(x_train, y_train)" part of the code. This is the issue "FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
TypeError: only size-1 arrays can be converted to Python scalars". Do you know how to fix this? I have ensured to use the correct versions (scikit learn, scikit image, and numpy) as shown in video, and followed the video step by step. This is the code for importing images i have (i had to add dtype=object when converting lists to numpy array as it was giving me visibleDeprecationWarning): for category_idx, category in enumerate(categories):
for file in os.listdir(os.path.join(input_dir, category)):
img_path = os.path.join(input_dir, category, file)
img = imread(img_path)
print(len(data))
img = resize(img, (200, 200))
data.append(img.flatten())
labels.append(category_idx)
data = np.asarray(data, dtype=object)
labels = np.asarray(labels, dtype=object). Finally, do you have an instagram or other site to discuss this issue, if possible?
Hey, what is your Python version and your OS? Many things could be going on. I would start by making sure the data and labels are being loaded correctly. You could print 'data' and 'labels' and see if they make sense.
Great video, could I use this same code to classify more than 2 categories? or does it only classify in binary, like e.g empty not empty.
Hey, thank you! Yes, you can use it to classify more than 2 categories! 😃🙌
Great stuf!
😃 Thank you so much for your support!! 🙌🙌
@@ComputerVisionEngineer Your' welcome. Question: how can I contact you privately to hire you for an ai project I am running?
You are welcome to tell me about your project through discord. The link is in the about section.
i need your advice i have task where i need to find avarage pixal value of x-ray image object in x-ray image i have many object like pen paper palstic and so on and each intesity is different some are overlap some have no clear edegs and mixed edges can i detect or draw a boundry box around each object is it possible without ml model i am newbew in this field specially in image processing can it can be done by image process alone can i segement object if intesity of each object is different and edges are not so clear please help me i am single develpoer in my team
Hi Felipe, Actually I wanted to ask that is this model build using pretrained model or not? and If it is build on pre trained model can you suggest me a video of yours where I can learn building a model with using a pretrained model
Hi, I build this model using scikit learn, I am not using a pre trained model. 🙌
also i wanted to know about your video named as sign language detection with scikit learn. The thing is in that video the sign detection one of yours are you using any pre trained model?
@@mayankmalhotra3523 yes in that video the landmark detection is with a pre trained model.
@@ComputerVisionEngineer hey felipe i loved the tutorial but if i wanna see how the model recognises the parking spaces in a video and its real time working ? actually i wanted to see the output of it
Hello Sir, can we use this scikit in egg sexing for our incubator?
Hello , can u help me to know how go use my own data but i want to put an image and the code classify the image belong to any class , thank you
I have a task to classify cat and dog so i tried the same way you do it but the accuracy its very low i used gray scale to minimize the complicity but didont work , i want to used like this approach if you has any advice
Try changing the size you are resizing the images to, line 23 from file main.py. What is the size of your images?
@@ComputerVisionEngineer
I putted the size to (50,50) To make all image to one size but the image become a worst
@@odaithalji9603 by increasing the size in line 23 performance should go up. Nevertheless, this may not be the best approach for this type of task. I would recommend you to use feature extraction before applying a scikit learn classifier. I may do a video about it soon. 💪
@@ComputerVisionEngineer
Ok ,Thank you vary much,I will be waiting
Could you make another video when you test out this classifier?. Like host this online and let the user upload images of parking spaces and the program outputs 'empty' or 'not empty'. Thanks
I will try to. 🙌
hi um i dont know why i dont know how to get data i did get from desc but i still didnt know if you could tell me that would really help
hey , great Tutorial thank you , but how do i use it now to classify new images ?
Hey, oh I see the inference of individual images is not covered in this tutorial. Take a look at this video czcams.com/video/MJCSjXepaAM/video.html, in this video I also train a classifier using scikit learn and I show you how to inference new data. 🙌
@@ComputerVisionEngineer thank you so much
hi, qq how did you split the images to empty and not empty? I saw the other video where you cropped images, but how did you separate 6K+ images into empty and not empty, its not clear.
The opencv function I used to get all connected components provides an id for every component, so I looked at the ids of empty slots and non empty slots and save them into different directories.
@@ComputerVisionEngineer i see, thank you. Yes I also looked at the other videos you have and realized how its done. Thanks for responding. Btw I pinged you on linedin for a project. I did not see reply, assuming you are not logging in to that often. Would you be interested in a paid consultation?
@@user-vn1wy8qc3k thank you so much for reaching out regarding a paid consultation, I do work as a freelance contractor, but I am a little too busy right now with other projects. Yeah I don't use linkedin that much, but you can reach out to me through mail, discord, Patreon, or the comments in my videos, I try to answer as soon as possible. 🙌
@@ComputerVisionEngineer Filippe, if you can pass me your email that would be great. I don't know how to reach out on here without using comments.
Sir, I have facing some error while training the classifier. It says : ValueError: Found input variables with inconsistent numbers of samples: [4872, 1218]
It would be of great help if you solve this problem🙏
it'd be better if you put the snippet of code so we can check it out with you
cool video! can i possibly import this model to like a huskylens? thanks!
Hi, if you can execute Python and Scikit learn, then most likely yes! 🙌
@@ComputerVisionEngineer sounds great, trying to come up with a plan to computer vision something either its a good object or a bad one depending on some criteria
Can this prediction model be used for any set of data?
Sure, it is an image classifier. There are other approaches that may yield a higher accuracy, though.
If I have more that 2 categories of images, can I name “x_train” and “x_test” whatever I want? For example “car_train”, “car_test”, “bus_train”, “bus_test”, and “limo_train”, “limo_test”?
Hey, you can certainly name your variables whatever you want. But, remember the category is already encoded in the labels variable. For example if you are building a vehicle classifier and the categories are car, bus and limo. Then each data point will be labeled as 0 (car), 1(bus) or 2 (limo).
@@ComputerVisionEngineer ok cool. Thank you!
Thanks. Is there a way I can convert the model to a
tflite
Hey, the image classifier we used in this video is from scikit learn library, it is not possible to convert it to tflite. 🙌
@@ComputerVisionEngineer okay thanks a lot. I will be on the lookout for Keras and tensor flow. Thanks
Im confused, which part of the code tells it that the picture has a car or not?
Hello, can you help me to come up with python code that classify duck eggs such as balot, penoy, abnoy according to it's classification?
Hey, that sounds like a cool project! As long as the different classes are visually distinct, you can classify them with an image classifier! I suggest you start with the data collection; collect data from all the different categories you wish to classify. Once you have the data, the Python code would be very similar as the one I used in this video! 😃💪🦆
Thank you, sir. Could you share the dataset used?
Sure! I have added a link to the data in the description of this video! 😃
@@ComputerVisionEngineer Thanks, brother
How do you build up your trainig data? You won‘t have decided for 6000 pictures whether they are empty or not?
Did you use kind of a „step by step“ classification? So that with lets say with 50 pictures you built up a classifier, that builds up further (and of course checked it then whether it worked properly?
Thanks for your videos!
Hey Niklas! That is a good question.
The data I used in this video comes from a previous video where I built a parking space detector and counter. In that other video I got the location of all parking slots by getting all the connected components of a binary mask. The function I used for getting the connected components got me not only the bbox but also an id for that specific location. Then, as most of the spaces are either empty or not-empty during the entire video, I just selected a few empty spaces (and a few not-empty spaces) and cropped them out of the video in all frames. 💪💪
When i was trying to convert data and labels into numpy array, i got this error😢
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (84,) + inhomogeneous part.
What is this ?
Thanks for the amazing tutorial ❤
Hey, what is your OS and your Python version? Have you installed the requirements listed in the requirements.txt file?
@@ComputerVisionEngineer No sir, first i used a data set that i have created, when i load your data set, now it is working 😍
But I want to know what is that error ? How it occurred 🥲
Hello Sir, i used the same code, but its not giving me the output result, like don't print the accuracy... kindly help!
thats what it is showing in the notification area.
Shared indexes are downloaded for Python packages in 1 min, 12 sec (20.48 MB)
Hey, I just tested the code and everything seems to be working well on my end!
That msg seems to be related to Pycharm, maybe you could try to execute from another IDE or from the command line?
I'm experiencing the same, any suggestions for a solution?
where do i get model.p file?? it is not available in code nor data link given in description
Hey, in order to create the model.p file you need to execute 'main.py' from the project's repository. 🙌
not able to download dataset
Ciao Fillippe, I have question about this profession in 2024 and want to know your opinion. Do I need to study any course in online platforms to become a good on it and of course finde a job(take certificate for CV). And is it easy to find a job as Comp.Vision engineer nowadays(2024 and maybe future). What can you advise me to become an engineer as you (online courses, books ect...)
P.S. I really appreciate your contents, thank you for your time and effort. Hope to become a professional com.vision engineer and send you a gift from my 1st salary(but now i dont know, how to become it)
Hi, take a look at the computer vision roadmap video, there are links to youtube videos and resources you can use to learn computer vision engineering. 🙌
how if I need to use CNN network? Example AlexNet?
Hey, I may make another video in the future on creating an image classifier based on deep learning, like AlexNet. 😃💪
how to deploy your pickle model on android app?
Hey Endah, there are different ways in which you can deploy the model we created in Scikit learn in an Android app. If you are looking to run the model in the mobile device you could consider the Python library sklearn2onnx in order to convert the Scikit learn model into an onnx file. 😃💪
@@ComputerVisionEngineer do you have any reference to change the model? I had trouble finding the reference, I used this classification method for my thesis and my deadline is only 2 days to be able to run this program. I hope you can help me, thank you
Try to Google "deploy scikit learn model android app" there are a few articles on how to do it, you may find the solution in one of those articles. 😃
Que crack
I have 100.0% of samples were correctly classified 😁
Awesome! 😃🙌
Why don't you annotate the images using bounding box or any methoda ? Is it not compulsory ? 🥲
Hey, given this is an image classifier the type of annotation we need is only the image category. 💪💪