152 points by mayankdhamasia 1 year ago flag hide 22 comments
datawiz 1 year ago next
Interesting article on data augmentation! I've been using similar techniques to improve the accuracy of my models. Has anyone else experimented with different methods? #objectdetection
mlfan 1 year ago next
Yes, I've had success with random cropping and color jitter. However, overfitting can still be a problem if you're not careful with the number of augmented samples. #dataaugmentation #objectdetection
ai_expert 1 year ago prev next
I use a technique called mixup which combines two images to create a new data point. It's been very effective for me. #datamixing #objectdetection
deeplearning 1 year ago prev next
What libraries do you all use for data augmentation? I've been using TensorFlow's `tf.image` module for most of my projects.
torchuser 1 year ago next
If you're using PyTorch, the `torchvision.transforms` module is quite convenient. #pytorch #objectdetection
computervision 1 year ago prev next
I often use the `imgaug` library, it has a lot of useful data augmentation techniques. #computervision #objectdetection
opencv_enthusiast 1 year ago prev next
`OpenCV` also provides some data augmentation functions. #opencv #objectdetection
ai_engineer 1 year ago prev next
I recommend using a validation set with augmented data to make sure your model isn't overfitting on the augmented samples. #machinelearning #objectdetection
datascientist 1 year ago prev next
One thing to keep in mind is to ensure the data augmentation transformation does not change the ground truth bounding boxes for object detection tasks. #objectdetection
ml_researcher 1 year ago prev next
Some recent papers have suggested using adversarial data augmentation for improved robustness. Thoughts? #machinelearning #objectdetection
reinforcement_learner 1 year ago next
Adversarial data augmentation can indeed help improve the model's robustness, but it may not always translate to better performance in practice. #reinforcementlearning
computervision 1 year ago prev next
True, adversarial data augmentation can also be more computationally expensive compared to traditional data augmentation methods. #computervision
dataengineer 1 year ago prev next
How do you all handle augmentation when dealing with large datasets? Any best practices to share? #bigdata #objectdetection
databricks_user 1 year ago next
I usually implement data augmentation as part of the data pipeline, either using Spark's `map` function or `HorovodRunner` for distributed training. #distributedtraining #objectdetection
aws_data_engineer 1 year ago prev next
You can use `Amazon SageMaker` for data augmentation and distributed training, making it easier to handle large datasets. #sagemaker #objectdetection
researcher 1 year ago prev next
Do you know of any good resources or papers on automating data augmentation? #machinelearning #objectdetection
ml_student 1 year ago next
This paper by Cubuk et al. on AutoAugment discusses automating data augmentation policies using reinforcement learning: <https://arxiv.org/pdf/1805.09501.pdf> #machinelearning
ai_intern 1 year ago prev next
RandAugment is another method for automatic data augmentation, which is simpler and faster than AutoAugment. Check it out: <https://arxiv.org/pdf/1909.13719.pdf> #ai
reinforcement_learner 1 year ago prev next
Keep in mind that while automated data augmentation methods can save time, they may not always produce optimal policies. It's essential to evaluate and fine-tune the policies for your specific application. #machinelearning
ai_developer 1 year ago prev next
For real-world applications, do you use unsupervised data augmentation techniques to create synthetic training data, or do you prefer other methods? #syntheticdata #objectdetection
computervision 1 year ago next
Synthetic data is helpful, especially when labeled data is scarce. However, the domain gap between synthetic and real data can cause performance issues. It's crucial to reduce the domain gap using domain adaptation techniques. #domainadaptation #objectdetection
datascientist 1 year ago prev next
Unsupervised data augmentation can be a good way to generate additional training data, but it's essential to regularly review the generated data to avoid introducing errors or biases. #datageneration #objectdetection