From 45ecb11402c2a02dd4f3f338a8dc3f3a563ace46 Mon Sep 17 00:00:00 2001 From: Victor Hall Date: Sun, 24 Mar 2024 10:02:38 -0400 Subject: [PATCH] update cog doc with colab link --- doc/CAPTION_COG.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/doc/CAPTION_COG.md b/doc/CAPTION_COG.md index c54bf4f..0befeb9 100644 --- a/doc/CAPTION_COG.md +++ b/doc/CAPTION_COG.md @@ -1,11 +1,13 @@ # CogVLM captioning -CogVLM [code](https://github.com/THUDM/CogVLM) [model](https://huggingface.co/THUDM/cogvlm-chat-hf) is, so far (Q1 2024), the best model for automatically generating captions. +CogVLM ([code](https://github.com/THUDM/CogVLM)) ([model](https://huggingface.co/THUDM/cogvlm-chat-hf)) is, so far (Q1 2024), the best model for automatically generating captions. -The model uses about 13.5GB of VRAM due to 4bit inference with the default setting of 1 beam, and up to 4 or 5 beams is possible with a 24GB GPU meaning it is very capable on consumer hardware. It is slow, ~6-10 seconds on a RTX 3090, but the quality is worth it over other models. +The model uses about 13.5GB of VRAM due to 4bit inference with the default setting of 1 beam, and up to 4 or 5 beams is possible with a 24GB GPU meaning it is very capable on consumer hardware. It is slow, ~6-10+ seconds on a RTX 3090, but the quality is worth it over other models. It is capable of naming and identifying things with proper nouns and has a large vocabulary. It can also readily read text even for hard to read fonts, from oblique angles, or from curved surfaces. +Open In Colab + ## Basics Run `python caption_cog.py --help` to get a list of options.