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Description
BurstPick serves as a desktop culling tool tailored for wildlife, bird, and action photographers who work with RAW burst images. This software efficiently scans a specified folder of photographs, automatically categorizing frames that were captured in quick succession into bursts, and assigns scores to each frame so that the best candidates are highlighted within every burst.
The scoring system utilizes six criteria specifically weighted for wildlife photography rather than portraiture: Focus (28%), Eye Visibility (22%), Exposure (17%), Composition (17%), Noise (11%), and Context (5%). Focus assesses the sharpness of the primary subject instead of the entire image; Eye Visibility evaluates if the subject's eye is both illuminated and in focus; Noise is sensitive to ISO levels, allowing for some grain while reducing penalties for banding. Users can adjust the weightings in real-time, save and export presets, and utilize a "Calibrate from my picks" feature that formulates a new weighting based on previously selected frames. Furthermore, a detailed breakdown for each criterion is provided to clarify the scoring process, enabling photographers to understand how their images are evaluated. This comprehensive approach not only streamlines the culling process but also enhances the overall workflow for photographers.
Description
RankLLM is a comprehensive Python toolkit designed to enhance reproducibility in information retrieval research, particularly focusing on listwise reranking techniques. This toolkit provides an extensive array of rerankers, including pointwise models such as MonoT5, pairwise models like DuoT5, and listwise models that work seamlessly with platforms like vLLM, SGLang, or TensorRT-LLM. Furthermore, it features specialized variants like RankGPT and RankGemini, which are proprietary listwise rerankers tailored for enhanced performance. The toolkit comprises essential modules for retrieval, reranking, evaluation, and response analysis, thereby enabling streamlined end-to-end workflows. RankLLM's integration with Pyserini allows for efficient retrieval processes and ensures integrated evaluation for complex multi-stage pipelines. Additionally, it offers a dedicated module for in-depth analysis of input prompts and LLM responses, which mitigates reliability issues associated with LLM APIs and the unpredictable nature of Mixture-of-Experts (MoE) models. Supporting a variety of backends, including SGLang and TensorRT-LLM, it ensures compatibility with an extensive range of LLMs, making it a versatile choice for researchers in the field. This flexibility allows researchers to experiment with different model configurations and methodologies, ultimately advancing the capabilities of information retrieval systems.
API Access
Has API
API Access
Has API
Screenshots View All
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Integrations
Gemini
Gemini Enterprise
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Qwen
RankGPT
Integrations
Gemini
Gemini Enterprise
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Qwen
RankGPT
Pricing Details
$89 one-time
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
BurstPick LLC
Founded
2026
Country
United States
Website
burstpick.com
Vendor Details
Company Name
Castorini
Country
Canada
Website
github.com/castorini/rank_llm/