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narugo1992
updated
a
dataset
about 7 hours ago
narugo1992
updated
a
dataset
about 11 hours ago
narugo1992
updated
a
dataset
about 19 hours ago
Post
270
🙋🏻♂️ hey there folks ,
So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :
basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!
this is terrible ! but the good news is that we can do something about it !
so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/
kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .
Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀
cc. @cyrilzakka
So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :
basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!
this is terrible ! but the good news is that we can do something about it !
so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/
kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .
Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀
cc. @cyrilzakka
prithivMLmods
posted
an
update
7 days ago
Post
4785
OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
-
- 2 replies
prithivMLmods
posted
an
update
8 days ago
Post
2151
Just made a demo for Cosmos-Reason1, a physical AI model that understands physical common sense and generates appropriate embodied decisions in natural language through long chain-of-thought reasoning. Also added video understanding support to it. 🤗🚀
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
✦ Try the demo here : prithivMLmods/DocScope-R1
⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.
To know more about it, visit the model card of the respective model. !!
wangsssssss
authored
a
paper
10 days ago
Post
2439
🙋🏻♂️ Hey there folks ,
Yesterday the world's first "Learn to Vibe Code" application was released .
As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .
You can try it out already :
https://vibe.takara.ai
and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀
Yesterday the world's first "Learn to Vibe Code" application was released .
As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .
You can try it out already :
https://vibe.takara.ai
and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀
AbstractPhil
posted
an
update
14 days ago
Post
334
With flan-t5-base and clip models as teachers; I have produced and successfully trained a dual-shunt cross-attention adapter archetype. This is NOT a lora.
This adapter is currently tasked with taking the T5-flan-base to guide the outputs of VIT-L-14 and/or VIT-bigG-14, and the opposite is equally usable and utilizable within the archetype. Meaning the CLIP_G can also guide the T5-FLAN-base.
These checkpoints were trained with 20 million synthetic human-templated captions, and they can be heavily improved by multiple languages, additional depiction context, and any sort of finetune task desired of the user that can be applied to the T5-flan-base with little to no training due to the adapter's functionality and accuracy.
VIT-L-14 adapters only took a couple hours on a colab a100 and the VIT-bigG-14 took about 4 hours. So you can rapidly adapt many of these in short periods of time with almost no additional overhead beyond the single t5-flan-base required. Each can be compiled, loaded, and offloaded.
This is a cross-attention system meant to shape encoded text after the output is received from the clip models and is very fast to inference - the t5-flan-base on the other hand isn't the fastest.
It's trained on a form of cooperative association with a series of complex losses designed specifically for this associative process.
This adapter has individual gating for tokenization context with a multitude of safeguards to prevent overfitting during rapid learning and can be paired with any number of additional other adapters.
I'm currently formatting the comfyui nodes that will allow easy conditioning shift to showcase the full power of this cooperative system's capability.
The comfyui nodes will be available here shortly, I just need to write them.
https://github.com/AbstractEyes/comfy-clip-shunts
This adapter is currently tasked with taking the T5-flan-base to guide the outputs of VIT-L-14 and/or VIT-bigG-14, and the opposite is equally usable and utilizable within the archetype. Meaning the CLIP_G can also guide the T5-FLAN-base.
These checkpoints were trained with 20 million synthetic human-templated captions, and they can be heavily improved by multiple languages, additional depiction context, and any sort of finetune task desired of the user that can be applied to the T5-flan-base with little to no training due to the adapter's functionality and accuracy.
VIT-L-14 adapters only took a couple hours on a colab a100 and the VIT-bigG-14 took about 4 hours. So you can rapidly adapt many of these in short periods of time with almost no additional overhead beyond the single t5-flan-base required. Each can be compiled, loaded, and offloaded.
This is a cross-attention system meant to shape encoded text after the output is received from the clip models and is very fast to inference - the t5-flan-base on the other hand isn't the fastest.
It's trained on a form of cooperative association with a series of complex losses designed specifically for this associative process.
This adapter has individual gating for tokenization context with a multitude of safeguards to prevent overfitting during rapid learning and can be paired with any number of additional other adapters.
I'm currently formatting the comfyui nodes that will allow easy conditioning shift to showcase the full power of this cooperative system's capability.
The comfyui nodes will be available here shortly, I just need to write them.
https://github.com/AbstractEyes/comfy-clip-shunts
-
- 1 reply
AbstractPhil
posted
an
update
15 days ago
Post
299
The T5-small + VIT-L-14 guidance shunt adapter is ready for toy use.
AbstractPhil/t5-vit-14-v1
Included is a simple drop-in for sdxl experimentation using colab.
The outcome is okay but not great - diffusers is a headache so I spent more time trying to disjoint that machine than I did actually messing with this adapter.
I trained two variations of the baseline adapter;
t5-small vanilla and t5-small-human-associated-try2-pass3.
The vanilla was more accurate to adding context while the human associated stays locked onto human topics like a bloodhound... badly. Both ended up being substandard, even with a robust adapter like this.
Finetunes with specific goals can complete at runtime if desired due to the t5-small's tiny size, clip_l's inference speed, and the adapter's size. The adapter is very small and has safeguards for overfitting that can be disabled, so runtime freezing and adaptive shifts can be a viable methodology to immediate task pipeline adaptation.
The t5-small lacks the behavioral complexity of a model more built for such a task such as the base, large, or xxl - or even the Flan T5-small. However, this doesn't slow the little brain slug down. It guides and it's wrappers have many rapid generation potentials, whether it's trained the way I trained it or not.
The proof of concept is there, and the outcomes are present. Judge yourself.
The next variation will be more dims, more catches, higher conv, and additional safeguards to prevent overfitting - as well as including considerably more laion flavors so the T5-flan-base doesn't overwhelm or vise-versa.
AbstractPhil/t5-vit-14-v1
Included is a simple drop-in for sdxl experimentation using colab.
The outcome is okay but not great - diffusers is a headache so I spent more time trying to disjoint that machine than I did actually messing with this adapter.
I trained two variations of the baseline adapter;
t5-small vanilla and t5-small-human-associated-try2-pass3.
The vanilla was more accurate to adding context while the human associated stays locked onto human topics like a bloodhound... badly. Both ended up being substandard, even with a robust adapter like this.
Finetunes with specific goals can complete at runtime if desired due to the t5-small's tiny size, clip_l's inference speed, and the adapter's size. The adapter is very small and has safeguards for overfitting that can be disabled, so runtime freezing and adaptive shifts can be a viable methodology to immediate task pipeline adaptation.
The t5-small lacks the behavioral complexity of a model more built for such a task such as the base, large, or xxl - or even the Flan T5-small. However, this doesn't slow the little brain slug down. It guides and it's wrappers have many rapid generation potentials, whether it's trained the way I trained it or not.
The proof of concept is there, and the outcomes are present. Judge yourself.
The next variation will be more dims, more catches, higher conv, and additional safeguards to prevent overfitting - as well as including considerably more laion flavors so the T5-flan-base doesn't overwhelm or vise-versa.
-
- 1 reply
prithivMLmods
posted
an
update
17 days ago
Post
2291
Got access to Google's all-new Gemini Diffusion a state-of-the-art text diffusion model. It delivers the performance of Gemini 2.0 Flash-Lite at 5x the speed, generating over 1000 tokens in a fraction of a second and producing impressive results. Below are some initial outputs generated using the model. ♊🔥
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion
Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true
🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
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- 1 reply
prithivMLmods
posted
an
update
18 days ago
Post
2297
The more optimized explicit content filters with lightweight 𝙜𝙪𝙖𝙧𝙙 models trained based on siglip2 patch16 512 and vit patch16 224 for illustration and explicit content classification for content moderation in social media, forums, and parental controls for safer browsing environments. this version fixes the issues in the previous release, which lacked sufficient resources. 🚀
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content
⤷ Building image safety-guard models :
strangerguardhf
⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0
⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
- 1 reply
AbstractPhil
posted
an
update
19 days ago
Post
584
Forcefeeding masked T5-Small 1 billion human-association captions to fry it's brain. I really don't know how long it'll take until I start nor do I know the logistic challenges I'll face when moving data from A to B, but the outcome should completely fry it and make it only fixate on human and diffusion responses. Should be a fun experiment that can just kind of run on automation.
The experiment's captions are available... mostly on my hf, I've had some rate limit problems that caused them to halt and I think I need to autogen another 100 million complex captions.
This WILL form heavy bias and burn-points. Random words will be peppered in the mix to allow the T5-Small to retain at least some semblance of what it was before I lobotomize it.
Likely I'll completely freeze half and burn the other half for a couple million as a test point. See how it takes or if it dies before 50k or something and need a refined process.
Oh great, even better. It didn't include the longer prompt variations. This won't start today.
Alright training began. I'm introducing a high degree variant of noise and chatter for the t5 to learn to bypass - while simultaneously increasing additional information output from the t5 in the process.
So far the outcome has been a degree of introduction for new information in the output. while simultaneously introducing rule of 3 parameterization into the T5 small.
I have high hopes.
The experiment's captions are available... mostly on my hf, I've had some rate limit problems that caused them to halt and I think I need to autogen another 100 million complex captions.
This WILL form heavy bias and burn-points. Random words will be peppered in the mix to allow the T5-Small to retain at least some semblance of what it was before I lobotomize it.
Likely I'll completely freeze half and burn the other half for a couple million as a test point. See how it takes or if it dies before 50k or something and need a refined process.
Oh great, even better. It didn't include the longer prompt variations. This won't start today.
Alright training began. I'm introducing a high degree variant of noise and chatter for the t5 to learn to bypass - while simultaneously increasing additional information output from the t5 in the process.
So far the outcome has been a degree of introduction for new information in the output. while simultaneously introducing rule of 3 parameterization into the T5 small.
I have high hopes.
-
- 3 replies
prithivMLmods
posted
an
update
23 days ago
Post
2699
Models for detecting images generated by diffusion models (Flux.1, SDXL, ..) are trained or fine-tuned using image classification models for content moderation. These models use datasets available on the Hub. For identifying AI-generated images or moderating visual content, the recommended model is OpenSDI-Flux.1-SigLIP2.😺🧨
Models : prithivMLmods/OpenSDI-Flux.1-SigLIP2 [Best approach for AI [Diffusion Generated] vs. real image classification] prithivMLmods/OpenSDI-SD2.1-SigLIP2 prithivMLmods/OpenSDI-SD3-SigLIP2 prithivMLmods/OpenSDI-SD1.5-SigLIP2 prithivMLmods/OpenSDI-SDXL-SigLIP2
Datasets : nebula/OpenSDI_test madebyollin/megalith-10m
Collection : prithivMLmods/opensdi-diffusion-generated-image-classification-682488a3a3e5be7083db3383
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
Models : prithivMLmods/OpenSDI-Flux.1-SigLIP2 [Best approach for AI [Diffusion Generated] vs. real image classification] prithivMLmods/OpenSDI-SD2.1-SigLIP2 prithivMLmods/OpenSDI-SD3-SigLIP2 prithivMLmods/OpenSDI-SD1.5-SigLIP2 prithivMLmods/OpenSDI-SDXL-SigLIP2
Datasets : nebula/OpenSDI_test madebyollin/megalith-10m
Collection : prithivMLmods/opensdi-diffusion-generated-image-classification-682488a3a3e5be7083db3383
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
prithivMLmods
posted
an
update
24 days ago
Post
2029
Dropping some image classification models for content moderation and classifiers trained with datasets available on the Hub. All are fine-tuned on the
🤗Models :
AI or Not : prithivMLmods/AIorNot-SigLIP2
Driver Drowsiness Detection : prithivMLmods/DOZE-GUARD-RLDD
Subset 10 ImageNet : prithivMLmods/IMAGENETTE
🥊Datasets :
+ competitions/aiornot
+ akahana/Driver-Drowsiness-Dataset
+ frgfm/imagenette
🔗Collection :
[The previous collection of models is also listed in the same collection, so you can find more models focused on image classification tasks.]
- prithivMLmods/multiclass-image-classification-05142025-68234c8010a9350a4d6739b5
Find a collections inside the collection.🤪👆
To know more about it, visit the model card of the respective model.
siglip2 backbone, (competitions AIOrNot, Imagenette, and Driver-Drowsiness). Models and datasets are listed below:🤗Models :
AI or Not : prithivMLmods/AIorNot-SigLIP2
Driver Drowsiness Detection : prithivMLmods/DOZE-GUARD-RLDD
Subset 10 ImageNet : prithivMLmods/IMAGENETTE
🥊Datasets :
+ competitions/aiornot
+ akahana/Driver-Drowsiness-Dataset
+ frgfm/imagenette
🔗Collection :
[The previous collection of models is also listed in the same collection, so you can find more models focused on image classification tasks.]
- prithivMLmods/multiclass-image-classification-05142025-68234c8010a9350a4d6739b5
Find a collections inside the collection.🤪👆
To know more about it, visit the model card of the respective model.
ImranzamanML
posted
an
update
26 days ago
Post
550
Run LLM model Locally using Docker right inside your codebase (No GUI Needed!)
In this project, I did not used the suporting GUI like Open WebUI or LM Studio or any other, so the purpose to use stand alone LLM models with ollama to give you the idea that how you can use it in your project/code instead of running through third party. Everything is containerized with Docker, so setup is clean and repeatable. Its just a fun side project so my connections can learn more about running models locally in their own projects.
Tech stack used:
🐋 Docker
🦙 LLaMA via Ollama
💻 HTML/CSS/JS
🐍 Python + FastAPI
🌐 NGINX
Its still early and a fun side project, but if you are into local model deployment, or just want to see how it works, check it out on the given link!
https://github.com/Imran-ml/llama-chatbot-dockerized
#LLM #Docker #OpenSource #Chatbot #LLaMA #fastapi
In this project, I did not used the suporting GUI like Open WebUI or LM Studio or any other, so the purpose to use stand alone LLM models with ollama to give you the idea that how you can use it in your project/code instead of running through third party. Everything is containerized with Docker, so setup is clean and repeatable. Its just a fun side project so my connections can learn more about running models locally in their own projects.
Tech stack used:
🐋 Docker
🦙 LLaMA via Ollama
💻 HTML/CSS/JS
🐍 Python + FastAPI
🌐 NGINX
Its still early and a fun side project, but if you are into local model deployment, or just want to see how it works, check it out on the given link!
https://github.com/Imran-ml/llama-chatbot-dockerized
#LLM #Docker #OpenSource #Chatbot #LLaMA #fastapi
prithivMLmods
posted
an
update
28 days ago
Post
3526
Dropping some image classification models for content moderation, balancers, and classifiers trained on synthetic datasets—along with others based on datasets available on the Hub. Also loaded a few low-rank datasets for realistic gender portrait classification and document-type classifiers, all fine-tuned on the SigLIP-2 Patch-16 224 backbone. Models and datasets are listed below:
🤗Models & Datasets :
Realistic Gender Classification : prithivMLmods/Realistic-Gender-Classification
⎙ prithivMLmods/Realistic-Portrait-Gender-1024px
Document Type Detection : prithivMLmods/Document-Type-Detection
⎙ prithivMLmods/Document-Type-Detection
Face Mask Detection : prithivMLmods/Face-Mask-Detection
⎙ DamarJati/Face-Mask-Detection
Alzheimer Stage Classifier : prithivMLmods/Alzheimer-Stage-Classifier
⎙ SilpaCS/Augmented_alzheimer
Bone Fracture Detection : prithivMLmods/Bone-Fracture-Detection
⎙ Hemg/bone-fracture-detection
GiD Land Cover Classification : prithivMLmods/GiD-Land-Cover-Classification
⎙ jonathan-roberts1/GID
🤗Collection : prithivMLmods/siglip2-05102025-681c2b0e406f0740a993fc1c
To know more about it, visit the model card of the respective model.
🤗Models & Datasets :
Realistic Gender Classification : prithivMLmods/Realistic-Gender-Classification
⎙ prithivMLmods/Realistic-Portrait-Gender-1024px
Document Type Detection : prithivMLmods/Document-Type-Detection
⎙ prithivMLmods/Document-Type-Detection
Face Mask Detection : prithivMLmods/Face-Mask-Detection
⎙ DamarJati/Face-Mask-Detection
Alzheimer Stage Classifier : prithivMLmods/Alzheimer-Stage-Classifier
⎙ SilpaCS/Augmented_alzheimer
Bone Fracture Detection : prithivMLmods/Bone-Fracture-Detection
⎙ Hemg/bone-fracture-detection
GiD Land Cover Classification : prithivMLmods/GiD-Land-Cover-Classification
⎙ jonathan-roberts1/GID
🤗Collection : prithivMLmods/siglip2-05102025-681c2b0e406f0740a993fc1c
To know more about it, visit the model card of the respective model.
prithivMLmods
posted
an
update
about 1 month ago
Post
3265
Well, here’s the updated version with the 20,000+ entry sampled dataset for Watermark Filter Content Moderation models incl. [Food25, Weather, Watermark, Marathi/Hindi Sign Language Detection], post-trained from the base models: sigLip2 patch16 224 — now with mixed aspect ratios for better performance and reduced misclassification. 🔥
Models :
➮ Watermark-Detection : prithivMLmods/Watermark-Detection-SigLIP2
⌨︎ Watermark Detection & Batch Image Processing Experimentals, Colab Notebook : https://colab.research.google.com/drive/1mlQrSsSjkGimUt0VyRi3SoWMv8OMyvw3?usp=drive_link
➮ Weather-Image-Classification : prithivMLmods/Weather-Image-Classification
➮ TurkishFoods-25 : prithivMLmods/TurkishFoods-25
➮ Marathi-Sign-Language-Detection : prithivMLmods/Marathi-Sign-Language-Detection
➮ Hindi-Sign-Language-Detection : prithivMLmods/Hindi-Sign-Language-Detection
Datasets :
Watermark : qwertyforce/scenery_watermarks
Weather : prithivMLmods/WeatherNet-05-18039
Turkish Foods 25 : yunusserhat/TurkishFoods-25
Marathi Sign Language : VinayHajare/Marathi-Sign-Language
Hindi Sign Language : Vedant3907/Hindi-Sign-Language-Dataset
Collection : prithivMLmods/content-filters-siglip2-vit-68197e3357d4de18fb3b4d2b
Models :
➮ Watermark-Detection : prithivMLmods/Watermark-Detection-SigLIP2
⌨︎ Watermark Detection & Batch Image Processing Experimentals, Colab Notebook : https://colab.research.google.com/drive/1mlQrSsSjkGimUt0VyRi3SoWMv8OMyvw3?usp=drive_link
➮ Weather-Image-Classification : prithivMLmods/Weather-Image-Classification
➮ TurkishFoods-25 : prithivMLmods/TurkishFoods-25
➮ Marathi-Sign-Language-Detection : prithivMLmods/Marathi-Sign-Language-Detection
➮ Hindi-Sign-Language-Detection : prithivMLmods/Hindi-Sign-Language-Detection
Datasets :
Watermark : qwertyforce/scenery_watermarks
Weather : prithivMLmods/WeatherNet-05-18039
Turkish Foods 25 : yunusserhat/TurkishFoods-25
Marathi Sign Language : VinayHajare/Marathi-Sign-Language
Hindi Sign Language : Vedant3907/Hindi-Sign-Language-Dataset
Collection : prithivMLmods/content-filters-siglip2-vit-68197e3357d4de18fb3b4d2b
ImranzamanML
authored
a
paper
about 1 month ago
prithivMLmods
posted
an
update
about 1 month ago
Post
1178
The new versions of Midjourney Mix adapters have been dropped in stranger zone hf. These adapters excel in studio lighting portraits and painterly styles, trained using the style of
strangerzonehf/Flux-Midjourney-Mix2-LoRA. They leverage 24-bit colored synthetic images generated form midjourney v6 to achieve high-quality image reproducibility and support adaptable aspect ratios, using Flux.1 as the base model. 🥳
Models [ ⌗ ]
> Flux-Midjourney-Painterly-LoRA : strangerzonehf/Flux-Midjourney-Painterly-LoRA
> Flux-Midjourney-Studio-LoRA : strangerzonehf/Flux-Midjourney-Studio-LoRA
> Collection : strangerzonehf/midjourney-mix-3-ft-flux1-dev-68165d58a2a08025852d63f3
> Space : prithivMLmods/FLUX-LoRA-DLC2
The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.
Models [ ⌗ ]
> Flux-Midjourney-Painterly-LoRA : strangerzonehf/Flux-Midjourney-Painterly-LoRA
> Flux-Midjourney-Studio-LoRA : strangerzonehf/Flux-Midjourney-Studio-LoRA
> Collection : strangerzonehf/midjourney-mix-3-ft-flux1-dev-68165d58a2a08025852d63f3
> Space : prithivMLmods/FLUX-LoRA-DLC2
The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.