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Machine Learning Image Recognition: Identifying Cats vs Dogs with 95% Accuracy(towarddatascience.com)

750 points by ai_engineer 6 months ago flag hide 13 comments

user1 6 months ago next
Great article! I've been working on a similar project lately, any tips on how to improve the accuracy?
- author 6 months ago next
  Sure! One thing you could try is using data augmentation to increase the size of your training set. This can help the model generalize better.
- user2 6 months ago prev next
  I've been struggling with overfitting in my model. Any suggestions on how to address this issue?
user3 6 months ago prev next
It's impressive that you were able to achieve 95% accuracy. What kind of dataset did you use?
- author 6 months ago next
  I used the Dogs vs. Cats dataset from Kaggle. It has over 25,000 images of dogs and cats, which was enough to train the model thoroughly.
user4 6 months ago prev next
What kind of neural network architecture did you use for this project?
- author 6 months ago next
  I used a convolutional neural network (CNN) with three convolutional layers followed by two fully connected layers. I also used a batch normalization layer after each convolutional layer to improve the stability of the model.
user5 6 months ago prev next
I tried using a similar CNN architecture, but I only got about 85% accuracy. Any ideas why my results might be worse than yours?
- author 6 months ago next
  It's possible that you may need to adjust the hyperparameters of your model to better fit your dataset. You could try decreasing the learning rate, adjusting the batch size, or using a different optimization algorithm.
user6 6 months ago prev next
What kind of hardware did you use to train your model? Did you use a GPU or just a CPU?
- author 6 months ago next
  I used a GTX 1080 Ti graphics card to train my model. Using a GPU can speed up the training process significantly.
user7 6 months ago prev next
Thanks for the article! I'm new to machine learning and image recognition, and this was a great tutorial for getting started.
- author 6 months ago next
  I'm glad you found it helpful! Machine learning can be a bit overwhelming at first, but it's really rewarding once you start to see results. Good luck with your projects!

user1 6 months ago next
Great article! I've been working on a similar project lately, any tips on how to improve the accuracy?
- author 6 months ago next
  Sure! One thing you could try is using data augmentation to increase the size of your training set. This can help the model generalize better.
- user2 6 months ago prev next
  I've been struggling with overfitting in my model. Any suggestions on how to address this issue?
user3 6 months ago prev next
It's impressive that you were able to achieve 95% accuracy. What kind of dataset did you use?
- author 6 months ago next
  I used the Dogs vs. Cats dataset from Kaggle. It has over 25,000 images of dogs and cats, which was enough to train the model thoroughly.
user4 6 months ago prev next
What kind of neural network architecture did you use for this project?
- author 6 months ago next
  I used a convolutional neural network (CNN) with three convolutional layers followed by two fully connected layers. I also used a batch normalization layer after each convolutional layer to improve the stability of the model.
user5 6 months ago prev next
I tried using a similar CNN architecture, but I only got about 85% accuracy. Any ideas why my results might be worse than yours?
- author 6 months ago next
  It's possible that you may need to adjust the hyperparameters of your model to better fit your dataset. You could try decreasing the learning rate, adjusting the batch size, or using a different optimization algorithm.
user6 6 months ago prev next
What kind of hardware did you use to train your model? Did you use a GPU or just a CPU?
- author 6 months ago next
  I used a GTX 1080 Ti graphics card to train my model. Using a GPU can speed up the training process significantly.
user7 6 months ago prev next
Thanks for the article! I'm new to machine learning and image recognition, and this was a great tutorial for getting started.
- author 6 months ago next
  I'm glad you found it helpful! Machine learning can be a bit overwhelming at first, but it's really rewarding once you start to see results. Good luck with your projects!