Banana.dev VS Papers with Code

Compare Banana.dev VS Papers with Code and see what are their differences

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Banana.dev

Banana provides inference hosting for ML models in three easy steps and a single line of code.

Papers with Code

The latest in machine learning at your fingerprints

Landing page //
2023-07-25

Landing page //
2022-07-17

Banana.dev

Website: banana.dev
Pricing URL: Official Banana.dev Pricing

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Papers with Code

Website: paperswithcode.com
Pricing URL: -

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Banana.dev features and specs

Ease of Use
Banana.dev offers a user-friendly interface, which allows developers to deploy and scale machine learning models easily without needing extensive infrastructure knowledge.
Scalability
The platform supports automatic scaling, which ensures that applications can handle increased loads without manual intervention.
Cost Efficiency
By automating infrastructure management, Banana.dev may reduce operational costs, making it a potentially more affordable option for startups and small companies.
Integration
Banana.dev provides easy integration with popular ML frameworks and tools, allowing for a seamless workflow from development to deployment.

Possible disadvantages of Banana.dev

Limited Customization
The platform's abstraction might limit the amount of customization available to users, which can be a downside for complex or highly specific requirements.
Dependency on Platform
Relying heavily on Banana.dev may lead to vendor lock-in, making it difficult to migrate workloads to other platforms if needed.
Potential Hidden Costs
While cost-efficient for many use cases, unexpected fees might arise due to scaling or additional services, making budgeting challenging.
Learning Curve
Despite its ease of use, there may still be a learning curve for those unfamiliar with deploying ML models, potentially requiring some upfront investment in training.

Papers with Code features and specs

Open Access
Papers with Code provides free access to a vast repository of research papers and code implementations, making cutting-edge research available to a wider audience.
Reproducibility
By linking research papers with their corresponding code, it promotes reproducibility, allowing researchers to verify results and build upon previous work more effectively.
Benchmarking
The platform offers benchmarking tools and leaderboards, facilitating the comparison of different models and approaches on standard datasets and fostering competition in the research community.
Community Engagement
Researchers and developers can contribute their own code and evaluations, which encourages community collaboration and the sharing of knowledge.
Resource Saving
By providing implementations and datasets, it saves researchers time and resources, enabling them to focus on innovation rather than recreating existing work.

Possible disadvantages of Papers with Code

Quality Control
Not all code implementations are thoroughly vetted or peer-reviewed, which can lead to issues with code quality and reliability.
Misalignment of Benchmarks
Benchmarks and evaluations might not perfectly align with certain niche or novel research tasks, potentially skewing perceptions about model performance.
Dependence on Contributor Participation
The platform relies heavily on community contributions; if participation wanes, the updates and breadth of resources could stagnate.
Integration Challenges
Integrating and adapting third-party code into different environments or existing projects can sometimes be challenging due to dependencies or compatibility issues.
Information Overload
With a vast amount of available papers and code, navigating and finding the most relevant and high-quality resources can be overwhelming for users.

Banana.dev videos

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Papers with Code videos

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The best site for research papers with codes on Machine/Deep Learning | Research paper search

Category Popularity

0-100% (relative to Banana.dev and Papers with Code)

Papers with Code

29 29%

71% 71

Developer Tools

43 43%

Developer Tools

57% 57

Data Science And Machine Learning

40 40%

Data Science And Machine Learning

60% 60

Productivity

100 100%

Productivity

0% 0

User comments

Share your experience with using Banana.dev and Papers with Code. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Papers with Code should be more popular than Banana.dev. It has been mentiond 99 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Banana.dev mentions (13)

Ask HN: How does deploying a fine-tuned model work
For the inference part, you can dockerise your model and use https://banana.dev for serverless GPU. They have examples on github on how to deploy and I’ve done it last year and was pretty straightforward. - Source: Hacker News / about 1 year ago
Authenticating requests sent to backend with middleware
I want to first check the user's ID and only if the user has an active subscription then the request will be forwarded to my API on banana.dev else the request will be blocked at the middleware itself. Should I use Express JS for the middleware i.e. Authentication and forwarding requests? Is there any other better way to improve my project structure? Currently it looks like:. Source: over 1 year ago
Ask HN: What do you use for ML Hosting
Hey! Would love to have you try https://banana.dev (bias: I'm one of the founders). We run A100s for you and scale 0->1->n->0 on demand, so you only pay for what you use. I'm at erik@banana.dev if you want any help with it :). - Source: Hacker News / about 2 years ago
Set up serverless GPU
CAN you do this in AWS? Of course, do they have a service that does exactly what this banana.dev does? Probably not. Source: about 2 years ago
Serverless GPU like banana.dev on AWS
I've been using banana.dev for easily running my ML models on GPU in a serverless manner, and interacting with them as an API. Although the principle of the service is sound, it is currently too buggy to take into production (very long cold boots, errorring requests, always hitting capacity). Source: about 2 years ago

Papers with Code mentions (99)

Computer Vision Made Simple with ReductStore and Roboflow
An helpful approach is to browse the state of the art models in paperswithcode. This will give you an idea of the performance of different models on various tasks. - Source: dev.to / 8 months ago
Show HN: Simple Science – The Newest Science Explained Simply
I think a way around this would some sort of voting/ popularity system? Papers with code (https://paperswithcode.com/) does this via Github stars sorting. Sure it doesn't mean something is established. But it at least gives some way to filter through the firehose of papers. Love this project btw! I think it has potential (and the timing is right now that everyone is looking for the next "attention is all... - Source: Hacker News / 9 months ago
Essential Deep Learning Checklist: Best Practices Unveiled
Adapting to Evolving Standards: With the rapid progress in deep learning research and applications, staying current with the latest developments is crucial. The checklist underscores the importance of considering established standard architectures and leveraging current state-of-the-art (SOTA) resources, like paperswithcode.com, to guide project decisions. This dynamic approach ensures that projects benefit from... - Source: dev.to / 11 months ago
Understanding Technical Research Papers
Papers With Code is one of the good resources to get you to get started. - Source: dev.to / about 1 year ago
Ask HN: Is there a data set for GitHub repos associated with academic papers?
For ML/DL papers you can check https://paperswithcode.com/. - Source: Hacker News / over 1 year ago