The clip-features model has a wide range of potential use cases in the field of computer vision and natural language processing. One possible use case is image classification, where the features extracted by the model can be used to classify images into different categories based on their visual and textual content. This can be useful in applications such as content moderation, image search, and recommendation systems. Additionally, the model can be used for object detection, where it can identify and localize objects within an image given a textual description. This can be applied in applications such as autonomous driving, surveillance systems, and augmented reality. Another use case is image generation, where the model can generate images based on a given text prompt, allowing for creative applications such as artwork generation, virtual world creation, and design optimization. Overall, the clip-features model has the potential to be a powerful tool for various practical applications that involve the analysis and understanding of both textual and visual information.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Llama 2 13b Embeddings||$?||172,645|
|Codellama 7b Instruct Gguf||$?||46|
|Llama 2 13b Chat Gguf||$?||1,352|
|Codellama 34b Instruct Gguf||$?||60|
You can use this area to play around with demo applications that incorporate the Clip Features model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Clip Features|
Return CLIP features for the clip-vit-large-patch14 model
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.00055|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||1 seconds|