OminiControl: Minimal and Universal Control for Diffusion Transformer
🔗 Discover More:
* Source Code: GitHub
* Try Demo: Try it here
* Paper Page: Read Paper
@Machine_learn
🔗 Discover More:
* Source Code: GitHub
* Try Demo: Try it here
* Paper Page: Read Paper
@Machine_learn
❤1
Forwarded from Github LLMs
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👍1
📑Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
👍2🔥1
📚Book Chapter:
Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences
📎 Study
@Machine_learn
Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences
📎 Study
@Machine_learn
👍1
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D-FINE
# Create env via conda
conda create -n dfine python=3.11.9
conda activate dfine
# Install requirements for inference
pip install -r tools/inference/requirements.txt
# Install ONNX
pip install onnx onnxsim
# Choose a model
export model=l # s, m, x
# Inference
python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg # video.mp4
@Machine_learn
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NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.
@Machine_learn
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👍4
polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
https://www.nature.com/articles/s41467-023-39868-6.pdf
@Machine_learn
https://www.nature.com/articles/s41467-023-39868-6.pdf
@Machine_learn
👍1🔥1
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language
Models
https://arxiv.org/pdf/2411.12886
@Machine_learn
Models
https://arxiv.org/pdf/2411.12886
@Machine_learn
2DMatGMM: An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
🖥 Github: https://github.com/jaluus/2dmatgmm
📕 Paper: https://arxiv.org/abs/2412.09333v1
⭐️ Dataset: https://paperswithcode.com/task/instance-segmentation
@Machine_learn
@Machine_learn
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OASIS Alzheimer's Detection
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
@Machine_learn
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
@Machine_learn
❤1
Forwarded from Papers
با عرض سلام نفر ۳ از مقاله زیر رو نیاز داریم.
Title: hybrid deep learnings and machine learning frameworks
for air quality prediction
during the COVID‑19 pandemic
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
در این مقاله تاثیر ۲۶ مدل ansemble و ترکیبی رو برای پیش بینی کیفیت هوا در بازه ۱ روزه ۳ روزه و ۷ روزه بررسی کردیم. جهت شرکت در این مقاله به ایدی بنده پیام بدین.
@Raminmousa
@Machine_learn
https://t.iss.one/+SP9l58Ta_zZmYmY0
Title: hybrid deep learnings and machine learning frameworks
for air quality prediction
during the COVID‑19 pandemic
journal: https://www.sciencedirect.com/journal/expert-systems-with-applications
if:7.5
در این مقاله تاثیر ۲۶ مدل ansemble و ترکیبی رو برای پیش بینی کیفیت هوا در بازه ۱ روزه ۳ روزه و ۷ روزه بررسی کردیم. جهت شرکت در این مقاله به ایدی بنده پیام بدین.
@Raminmousa
@Machine_learn
https://t.iss.one/+SP9l58Ta_zZmYmY0
Telegram
Papers
در اين كانال قرار مقالاتي كه كار ميكنيم رو به اشتراك بزاريم.
@Raminmousa
@Raminmousa