AI-Master-Book
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  • AI Master Book
    • 이상치 탐지 with Python
    • 베이지안 뉴럴네트워크 (BNN) with Python
    • 그래프 뉴럴네트워크 (GNN) with Python
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  • LLM MASTER BOOK
    • OpenAI API 쿡북 with Python
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    • MCP 에이전트 쿡북 with Python
  • LLMs
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      • 1️⃣ChatCompletion
      • 2️⃣DALL-E
      • 3️⃣Text to Speech
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      • 5️⃣Assistants API
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      • LangChain Basic
        • 1️⃣Basic Modules
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      • LangChain Intermediate
        • 1️⃣OpenAI LLM
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        • 9️⃣Expression Language(LCEL)
        • 🔟Llama3-8B with LangChain
      • LangChain Advanced
        • 1️⃣LLM Evaluation
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        • 7️⃣LangChain vs. LlamaIndex
        • 8️⃣LangChain LCEL vs. LangGraph
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      • LlamaIndex Basic
        • 1️⃣Introduction
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        • 3️⃣Data Connectors
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        • 5️⃣Naive RAG
        • 6️⃣Advanced RAG
        • 7️⃣Llama3-8B with LlamaIndex
        • 8️⃣LlmaPack
      • LlamaIndex Intermediate
        • 1️⃣QueryEngine
        • 2️⃣Agent
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        • 4️⃣Evaluation-Driven Development
        • 5️⃣Fine-tuning
        • 6️⃣Prompt Compression with LLMLingua
      • LlamaIndex Advanced
        • 1️⃣Agentic RAG: Router Engine
        • 2️⃣Agentic RAG: Tool Calling
        • 3️⃣Building Agent Reasoning Loop
        • 4️⃣Building Multi-document Agent
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      • Huggingface Basic
        • 1️⃣Datasets
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          • 1️⃣Sentiment Analysis
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          • 6️⃣Topic Modeling: BERTopic
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          • 8️⃣Summarization
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          • 🔟Text Generation
        • Audio & Tabular
          • 1️⃣Text-to-Speech: TTS
          • 2️⃣Speech Recognition: Whisper
          • 3️⃣Audio Classification
          • 4️⃣Tabular Qustaion & Answering
        • Vision & Multimodal
          • 1️⃣Image-to-Text
          • 2️⃣Text to Image
          • 3️⃣Image to Image
          • 4️⃣Text or Image-to-Video
          • 5️⃣Depth Estimation
          • 6️⃣Image Classification
          • 7️⃣Object Detection
          • 8️⃣Segmentatio
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        • 1️⃣Accelerator
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        • 3️⃣Flash Attention
        • 4️⃣Quantization
        • 5️⃣Safetensors
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        • 7️⃣Optimum-NVIDIA
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      • Huggingface Fine-tuning
        • 1️⃣Transformer Fine-tuning
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        • 3️⃣PEFT: Fine-tuning with QLoRA
        • 4️⃣PEFT: Fine-tuning Phi-2 with QLoRA
        • 5️⃣Axoltl Fine-tuning with QLoRA
        • 6️⃣TRL: RLHF Alignment Fine-tuning
        • 7️⃣TRL: DPO Fine-tuning with Phi-3-4k-instruct
        • 8️⃣TRL: ORPO Fine-tuning with Llama3-8B
        • 9️⃣Convert GGUF gemma-2b with llama.cpp
        • 🔟Apple Silicon Fine-tuning Gemma-2B with MLX
        • 🔢LLM Mergekit
    • Agentic LLM
      • Agentic LLM
        • 1️⃣Basic Agentic LLM
        • 2️⃣Multi-agent with CrewAI
        • 3️⃣LangGraph: Multi-agent Basic
        • 4️⃣LangGraph: Agentic RAG with LangChain
        • 5️⃣LangGraph: Agentic RAG with Llama3-8B by Groq
      • Autonomous Agent
        • 1️⃣LLM Autonomous Agent?
        • 2️⃣AutoGPT: Worldcup Winner Search with LangChain
        • 3️⃣BabyAGI: Weather Report with LangChain
        • 4️⃣AutoGen: Writing Blog Post with LangChain
        • 5️⃣LangChain: Autonomous-agent Debates with Tools
        • 6️⃣CAMEL Role-playing Autonomous Cooperative Agents
        • 7️⃣LangChain: Two-player Harry Potter D&D based CAMEL
        • 8️⃣LangChain: Multi-agent Bid for K-Pop Debate
        • 9️⃣LangChain: Multi-agent Authoritarian Speaker Selection
        • 🔟LangChain: Multi-Agent Simulated Environment with PettingZoo
    • Multimodal
      • 1️⃣PaliGemma: Open Vision LLM
      • 2️⃣FLUX.1: Generative Image
    • Building LLM
      • 1️⃣DSPy
      • 2️⃣DSPy RAG
      • 3️⃣DSPy with LangChain
      • 4️⃣Mamba
      • 5️⃣Mamba RAG with LangChain
      • 7️⃣PostgreSQL VectorDB with pgvorco.rs
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  • Image Classification
  • ViT
  1. LLMs
  2. Hugging Face
  3. Huggingface Tasks
  4. Vision & Multimodal

Image Classification

Image Classification

Image classification은 시각적 콘텐츠를 기반으로 이미지에 레이블 또는 클래스를 할당하는 작업이 포함됩니다.

from transformers import pipeline

clf = pipeline("image-classification")
clf("dataset/mountain.jpg")
No model was supplied, defaulted to google/vit-base-patch16-224 and revision 5dca96d (https://huggingface.co/google/vit-base-patch16-224).
Using a pipeline without specifying a model name and revision in production is not recommended.
/home/kubwa/anaconda3/envs/pytorch/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(



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[{'label': 'valley, vale', 'score': 0.5141904950141907},
 {'label': 'alp', 'score': 0.37611910700798035},
 {'label': 'mountain tent', 'score': 0.03428410366177559},
 {'label': 'volcano', 'score': 0.022554099559783936},
 {'label': 'lakeside, lakeshore', 'score': 0.004615743178874254}]

ViT

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

inputs = processor(
    images=image, 
    return_tensors="pt"
)
outputs = model(**inputs)
logits = outputs.logits

# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: Egyptian cat
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Last updated 1 year ago

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