Building an Image Recognition API using Flask.
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)
https://t.iss.one/DataScienceT
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)
https://t.iss.one/DataScienceT
❤🔥2😍2👍1
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🚀 Hierarchical Open-vocabulary Universal Image Segmentation
Decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".
🖥 Github: https://github.com/berkeley-hipie/hipie
📕 Paper: https://arxiv.org/abs/2307.00764v1
🔗Project: https://people.eecs.berkeley.edu/~xdwang/projects/HIPIE/
🔗 Dataset: https://paperswithcode.com/dataset/pascal-panoptic-parts
https://t.iss.one/DataScienceT
Decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".
🖥 Github: https://github.com/berkeley-hipie/hipie
📕 Paper: https://arxiv.org/abs/2307.00764v1
🔗Project: https://people.eecs.berkeley.edu/~xdwang/projects/HIPIE/
🔗 Dataset: https://paperswithcode.com/dataset/pascal-panoptic-parts
https://t.iss.one/DataScienceT
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🔍Foundation Model for Endoscopy Video Analysis
🖥 Github: https://github.com/med-air/endo-fm
⏩ Paper: https://arxiv.org/pdf/2306.16741v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/kumc
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/med-air/endo-fm
⏩ Paper: https://arxiv.org/pdf/2306.16741v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/kumc
https://t.iss.one/DataScienceT
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We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free
To request a subscription: talk to @Hussein_Sheikho
To request a subscription: talk to @Hussein_Sheikho
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🎨 Making ML-powered web games with Transformers.js
The goal of this tutorial is to show you how easy it is to make your own ML-powered web game.
🖥 Github: https://github.com/xenova/doodle-dash
🤗 Hugging face: https://huggingface.co/blog/ml-web-games
⭐️ Code: https://github.com/xenova/doodle-dash
🔗Demo: https://huggingface.co/spaces/Xenova/doodle-dash
🔗 Dataset: https://huggingface.co/datasets/Xenova/quickdraw-small
https://t.iss.one/DataScienceT
The goal of this tutorial is to show you how easy it is to make your own ML-powered web game.
🖥 Github: https://github.com/xenova/doodle-dash
🤗 Hugging face: https://huggingface.co/blog/ml-web-games
⭐️ Code: https://github.com/xenova/doodle-dash
🔗Demo: https://huggingface.co/spaces/Xenova/doodle-dash
🔗 Dataset: https://huggingface.co/datasets/Xenova/quickdraw-small
https://t.iss.one/DataScienceT
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🦙 Focused Transformer: Contrastive Training for Context Scaling
LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more.
🖥 Github: https://github.com/cstankonrad/long_llama
📕 Paper: https://arxiv.org/abs/2307.03170v1
🖥 Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
🔗 Dataset: https://paperswithcode.com/dataset/pg-19
https://t.iss.one/DataScienceT
LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more.
🖥 Github: https://github.com/cstankonrad/long_llama
📕 Paper: https://arxiv.org/abs/2307.03170v1
🖥 Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
🔗 Dataset: https://paperswithcode.com/dataset/pg-19
https://t.iss.one/DataScienceT
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ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation ♻️
🖥 Github: https://github.com/pendu/containergym
⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/pendu/containergym
⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
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🖥 Chat Downloader
A simple tool used to retrieve chat messages from livestreams, videos, clips and past broadcasts.
- YouTube.com
- Zoom.us
- Facebook.com
- Twitch.tv
🖥 Github
📝 Docs
https://t.iss.one/DataScienceT
A simple tool used to retrieve chat messages from livestreams, videos, clips and past broadcasts.
- YouTube.com
- Zoom.us
- Facebook.com
- Twitch.tv
$ pip install chat-downloader
Using:
# termimal
$ chat_downloader https://www.youtube.com/watch?v=video_link --output chat.json
# Python script
from chat_downloader import ChatDownloader
url = 'https://www.youtube.com/watch?v=video_link'
chat = ChatDownloader().get_chat(url)
for message in chat:
chat.print_formatted(message) 🖥 Github
📝 Docs
https://t.iss.one/DataScienceT
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🖥 Tkinter Designer
An easy and fast way to create a Python GUI 🐍
🖥 Github
https://t.iss.one/DataScienceT
An easy and fast way to create a Python GUI 🐍
🖥 Github
https://t.iss.one/DataScienceT
👍6❤2
Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification
🖥 Github: https://github.com/yuyongcan/benchmark-tta
⏩ Paper: https://arxiv.org/pdf/2307.03133v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/yuyongcan/benchmark-tta
⏩ Paper: https://arxiv.org/pdf/2307.03133v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
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🔎 DeepOnto: A Python Package for Ontology Engineering with Deep Learning
A package for ontology engineering with deep learning and language model.
pip install deeponto
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
A package for ontology engineering with deep learning and language model.
pip install deeponto
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
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Top 6 Algorithms Every Software Engineer Should Know
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
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