Hi,
whats the difference between pedestrian and people?
Arslan Mehmood
ArslanMehmood
AI & ML interests
Deep learning, computer vision
Recent Activity
repliedto dronefreak's post about 21 hours ago
Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome. liked a model about 2 months ago
nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 reacted to MaziyarPanahi's post with 🔥 3 months ago
We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvisionOrganizations
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replied to dronefreak's post about 21 hours ago
liked a
model about 2 months ago
reacted to MaziyarPanahi's post with 🔥 3 months ago
Post
2359
We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
- 2 replies
liked 4
models 8 months ago
liked a
model 12 months ago
liked 8
models over 1 year ago
vidore/colpali-v1.2
Visual Document Retrieval • Updated • 191k • 112
SWivid/F5-TTS
Text-to-Speech • Updated • 655k • 1.18k
fudan-generative-ai/hallo2
Updated • 137
jadechoghari/VidToMe
Text-to-Video • Updated • 4 • 9
zai-org/CogVideoX-5b
Text-to-Video • Updated • 15.2k • • 675
rhymes-ai/Allegro
Text-to-Video • Updated • 146 • 264
TMElyralab/MuseV
Text-to-Video • Updated • 137
GlitchXRiot/Seba-Ai
Text-to-Video • Updated • 2 • 2
upvoted a collection over 1 year ago
reacted to mitkox's post with 🔥 almost 2 years ago
Post
2458
Me: I want on device AI: fast, without latency, with real privacy, convenient for use and development.
Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.
Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.
Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
Microsoft: The best I can do is Copilot+. You need a special Qualcomm chip and Windows 11 24H2. Today I can give you only Recall, taking screenshots and running a visual model to write context about what you are doing in the unencrypted Semantic Index database for embeddings. I'm giving you SLMs Phi Silica, accessible only via API and SDK. In the autumn I can give you the developer tools for C#/C++ and you can use them.
Apple: The best I can do is Apple Intelligence. You need a special Apple chip and macOS 15. Today I can give you only marketing. In the autumn I can give you on-device 3B quantized to 3.5bit mysterious SLMs and diffusion models with LoRA adapters. We will have an encrypted Semantic Index database for embeddings and agentic flows with function calling. We will call all of them with different names. In the autumn I will give you the developer tools in Swift and you can use them.
Open Source: The best I can do is llama.cpp. You can run it on any chip and OS. Today you can run AI inferencing on device and add other open source components for your solution. I can give you local AI models SLMs/LLMs - from wqen2-0.5B to Llama3-70B. You can have an encrypted local embeddings database with PostgreSQL/pgvector or SQLite-Vec. I can give you a wide choice of integrations and open-source components for your solution- from UIs to agentic workflows with function calling. Today I can give you the developer tools in Python/C/C++/Rust/Go/Node.js/JS/C#/Scala/Java and you can use them.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
- 4 replies