Post
3986
Announcing RefCOCO-M, a refreshed RefCOCO with pixel-accurate masks and the problematic prompts removed.
moondream/refcoco-m
moondream/refcoco-m
Vik Korrapati PRO
vikhyatk
AI & ML interests
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Recent Activity
published
a dataset
1 day ago
moondream/brackish_underwater
updated
a dataset
1 day ago
moondream/brackish_underwater
published
a model
1 day ago
moondream/SegHeadRefiner
Organizations
posted
an
update
2 months ago
posted
an
update
4 months ago
Post
4980
Just released a preview of Moondream 3!
moondream/moondream3-preview
This is a 9B parameter, 2B active MoE VLM with state of the art visual reasoning capabilities.
More details in the release blog post: https://moondream.ai/blog/moondream-3-preview
This is a 9B parameter, 2B active MoE VLM with state of the art visual reasoning capabilities.
More details in the release blog post: https://moondream.ai/blog/moondream-3-preview
- 3 replies
posted
an
update
11 months ago
Post
6195
🚨 New VQA + captioning dataset!
moondream/megalith-mdqa
Images from Megalith, captioned using Moondream, then transformed to short-form QA.
9M+ images, 6-10 QA pairs per image.
Images from Megalith, captioned using Moondream, then transformed to short-form QA.
9M+ images, 6-10 QA pairs per image.
reacted to
Xenova's
post with ❤️🔥
about 1 year ago
Post
6096
Have you tried out 🤗 Transformers.js v3? Here are the new features:
⚡ WebGPU support (up to 100x faster than WASM)
🔢 New quantization formats (dtypes)
🏛 120 supported architectures in total
📂 25 new example projects and templates
🤖 Over 1200 pre-converted models
🌐 Node.js (ESM + CJS), Deno, and Bun compatibility
🏡 A new home on GitHub and NPM
Get started with
Learn more in our blog post: https://huggingface.co/blog/transformersjs-v3
⚡ WebGPU support (up to 100x faster than WASM)
🔢 New quantization formats (dtypes)
🏛 120 supported architectures in total
📂 25 new example projects and templates
🤖 Over 1200 pre-converted models
🌐 Node.js (ESM + CJS), Deno, and Bun compatibility
🏡 A new home on GitHub and NPM
Get started with
npm i @huggingface/transformers.Learn more in our blog post: https://huggingface.co/blog/transformersjs-v3
- 3 replies
posted
an
update
about 1 year ago
Post
6937
Just released a dataset with 7000+ hours of synthetically generated lo-fi music.
vikhyatk/lofi
posted
an
update
over 1 year ago
Post
6865
Pushed a new update to
vikhyatk/moondream2 today. TextVQA up from 60.2 to 65.2, DocVQA up from 61.9 to 70.5.
Space has been updated to the new model if you want to try it out! vikhyatk/moondream2
Space has been updated to the new model if you want to try it out! vikhyatk/moondream2
reacted to
Csplk's
post with 🔥
over 1 year ago
Post
2948
# Offensive Security Reconnaissance Continued with Public Facing Industrial Control System HMIs using Moondream
Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.
Using Moondream with batch processing ( Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS ( Csplk/ICS_UIs) HMI ( Csplk/HMI) screenshots from shodan to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including
1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**
Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before, offensive security with moondream focused HF Space once its fleshed out.
Thanks again to @vikhyatk for the incredible Moondream model. vikhyatk/moondream2
Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.
Using Moondream with batch processing ( Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS ( Csplk/ICS_UIs) HMI ( Csplk/HMI) screenshots from shodan to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including
1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**
Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before, offensive security with moondream focused HF Space once its fleshed out.
Thanks again to @vikhyatk for the incredible Moondream model. vikhyatk/moondream2
replied to
their
post
over 1 year ago
It's in the same repo, uploaded with the tag "2024-07-23" you can pass in as revision when instantiating the model.
replied to
their
post
over 1 year ago
Yup, currently working on getting a clean GGML implementation working here so I can then go and figure out what's going on in llama.cpp. https://github.com/vikhyat/moondream/tree/vik-ggml
posted
an
update
over 1 year ago
Post
3389
🚀 Exciting news! We've just launched "Thundermoon" - the latest version of Moondream, our open-source vision language model! 🌙
Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
* DocVQA: 61.9 (↑103%)
* TextVQA: 60.2 (↑5.2%)
* GQA: 64.9 (↑2.9%)
What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.
Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!
Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.
Curious to try it out? Try out the live demo here! https://moondream.ai/playground
Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
* DocVQA: 61.9 (↑103%)
* TextVQA: 60.2 (↑5.2%)
* GQA: 64.9 (↑2.9%)
What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.
Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!
Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.
Curious to try it out? Try out the live demo here! https://moondream.ai/playground
-
- 4 replies
reacted to
Csplk's
post with 🤯
over 1 year ago
Post
1476
# Offensive Physical Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream
After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed @vikhyatk
I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them.
---
vikhyatk/moondream2
vikhyatk/moondream2
After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton experiments using Moondream (as usual) based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory based theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power image-text-to-text model with minimal computational power needed @vikhyatk
I have started actually implementing a custom little tool with both static html space sand python gradio spaces on the go which I shall share as hf spaces when done them.
---
vikhyatk/moondream2
vikhyatk/moondream2
- 1 reply
posted
an
update
over 1 year ago
Post
3800
Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?
Try out the vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇️
https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb
Try out the vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇️
https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb
posted
an
update
over 1 year ago
Post
3148
Just released a new version of
vikhyatk/moondream2 - now supporting higher resolution images (up to 756x756)!
TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.
TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.
posted
an
update
over 1 year ago
Post
1830
Cool new dataset from
@isidentical
- https://huggingface.co/datasets/isidentical/moondream2-coyo-5M-captions
The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.
The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.
- 1 reply
posted
an
update
over 1 year ago
Post
3134
Updated the
vikhyatk/lnqa dataset to include images, so you no longer need to separately download them from OpenImages!
reacted to
radames's
post with ❤️🔥
almost 2 years ago
Post
2811
Following up on
@vikhyatk
's Moondream2 update and
@santiagomed
's implementation on Candle, I quickly put togheter the WASM module so that you could try running the ~1.5GB quantized model in the browser. Perhaps the next step is to rewrite it using https://github.com/huggingface/ratchet and run it even faster with WebGPU,
@FL33TW00D-HF
.
radames/Candle-Moondream-2
ps: I have a collection of all Candle WASM demos here radames/candle-wasm-examples-650898dee13ff96230ce3e1f
radames/Candle-Moondream-2
ps: I have a collection of all Candle WASM demos here radames/candle-wasm-examples-650898dee13ff96230ce3e1f
posted
an
update
almost 2 years ago
Post
3381
Released a new version of
vikhyatk/moondream2 today! Primarily focused on improving OCR and captioning (e.g. "Describe this image", "Describe this image in one sentence"), but also seeing general improvement across all benchmarks.
- 1 reply
posted
an
update
almost 2 years ago
Post
3927
Just released a notebook showing how to finetune moondream: https://github.com/vikhyat/moondream/blob/main/notebooks/Finetuning.ipynb