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ImageReward is the first general-purpose human preference reward model (RM) designed for evaluating text-to-image generation, introduced alongside the NeurIPS 2023 paper ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. Trained on 137k expert-annotated image pairs, ImageReward significantly outperforms existing scoring methods like CLIP, Aesthetic, and BLIP in capturing human visual preferences. It is provided as a Python package (image-reward) that enables quick scoring of generated images against textual prompts, with APIs for ranking, scoring, and filtering outputs. Beyond evaluation, ImageReward supports Reward Feedback Learning (ReFL), a method for directly fine-tuning diffusion models such as Stable Diffusion using human-preference feedback, leading to demonstrable improvements in image quality.

...Download the HTML help files for details in the link below. Tested cameras: Canon 5D, Canon 550D, Hero GoPro, Sony F3 with browser to MOV or MXF rewarp. - Supports 25p fps essence. - Can customize the Avid clip name and comment to the clip - Generates take names by file name order - Create sync point Avid locator - Use the Media Tool to get the new files into the bin. Download the help files for more info - https://sourceforge.net/projects/rlingest/files/Html%20help%20files/ Requirements: - Mac OS Lion 10.7 or greater - FFMPEG (with libx264) - http://ffmpeg.org - BMX Libs - https://sourceforge.net/projects/bmxlib - Sony XDCAM Browser (for Sony F3 files rewrap)

Man's best friend may fetch your paper every morning, but Buddy will fetch a dozen newspapers, clip out summaries of the top stories, stock quotes, sports scores, weather data and comics you want and paste them together into an uber-newspaper.