Compare Deepdream and OpenCV 
- Users report that Deepdream has a lower star rating of 3.8 compared to OpenCV's 4.5, indicating that OpenCV is generally viewed more favorably by its users.
- Reviewers mention that while both products cater to small businesses, Deepdream has a higher percentage of reviews from this segment at 53.8%, suggesting it may be more tailored to the needs of smaller enterprises.
- G2 users highlight that Deepdream scores lower in "Meets Requirements" with a rating of 7.4, while OpenCV excels with a score of 8.7, indicating that OpenCV better fulfills user expectations and needs.
- Users on G2 report that Deepdream struggles with ease of use, scoring 7.1, whereas OpenCV offers a more user-friendly experience with a score of 7.8, making it easier for users to navigate and utilize effectively.
- Reviewers mention that OpenCV shines in "Quality of Support" with a score of 8.4, while Deepdream's support quality is rated at 7.4, suggesting that users may find more reliable assistance with OpenCV.
- Users say that OpenCV's "Ease of Setup" is rated at 8.4, which is significantly higher than Deepdream's performance in this area, indicating that OpenCV may be quicker and simpler to implement for users.
The fact that here lies an engine that mimics the brain in terms of how we dream, or more precisely, how we hallucinate, is in fact, cool. It gives us an insight on how a Convolutional Neural Network works, by giving a vague visualization of its layer to...
Though it is a good case study for new technology, Deep Dream probably won't solve any *immediate* business problems or meet any customer needs (besides really fun web apps). In my opinion, it functions best as a learning tool for those who actually want to...
- It is open source, also easy to install and setup. - Powerful API for face detection and image processing. - Multiple language support.
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
The fact that here lies an engine that mimics the brain in terms of how we dream, or more precisely, how we hallucinate, is in fact, cool. It gives us an insight on how a Convolutional Neural Network works, by giving a vague visualization of its layer to...
- It is open source, also easy to install and setup. - Powerful API for face detection and image processing. - Multiple language support.
Though it is a good case study for new technology, Deep Dream probably won't solve any *immediate* business problems or meet any customer needs (besides really fun web apps). In my opinion, it functions best as a learning tool for those who actually want to...
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
