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---
title: ACCEPTIN - Telecom Site Quality Classification
emoji: 📡
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.31.0
app_file: app.py
pinned: false
license: mit
---
# 📡 ACCEPTIN - Telecom Site Quality Classification
AI-powered telecom site inspection using ConvNeXt transfer learning.
---
## 🚀 Deploying ACCEPTIN on Hugging Face Spaces
### 1. Prepare Your Project Directory
Ensure your project has the following structure:
```
ACCEPTIN/
├── app.py
├── requirements.txt
├── README.md
├── models/
│ └── telecom_classifier.pth
├── utils/
│ ├── data_utils.py
│ └── model_utils.py
└── ... (other files)
```
### 2. Create a New Space on Hugging Face
1. Go to [Hugging Face Spaces](https://huggingface.co/spaces).
2. Click **Create new Space**.
3. Fill in:
- **Space name**: acceptin (or your choice)
- **SDK**: Streamlit
- **Hardware**: CPU basic (free)
- **Visibility**: Public or Private
4. Click **Create Space**.
### 3. Prepare Your Files
- **requirements.txt**: List all dependencies (see below for example).
- **README.md**: This file, with the YAML header above.
- **Model File**: Place `telecom_classifier.pth` in a `models/` folder. If >10MB, use Git LFS.
#### Example requirements.txt
```
streamlit==1.31.0
torch==2.2.0
torchvision==0.17.0
Pillow==10.2.0
numpy==1.26.0
timm==0.9.12
opencv-python-headless==4.8.1.78
plotly==5.18.0
pandas==2.2.0
scikit-learn==1.4.0
matplotlib==3.8.0
seaborn==0.13.0
tqdm==4.66.0
```
### 4. (If Needed) Set Up Git LFS for Large Files
If your model file is large:
```bash
git lfs install
git lfs track "*.pth"
git add .gitattributes
```
### 5. Upload Your Files to the Space
**A. Web Interface**
- Go to your Space on Hugging Face.
- Click the **Files** tab.
- Upload all files and folders (`app.py`, `requirements.txt`, `README.md`, `models/`, `utils/`, etc.).
**B. Git Method (Recommended)**
```bash
git clone https://huggingface.co/spaces/YOUR_USERNAME/acceptin
cd acceptin
# Copy your files into this directory
# If using Git LFS:
git lfs install
git lfs track "*.pth"
git add .gitattributes
git add .
git commit -m "Initial ACCEPTIN deployment"
git push
```
### 6. Wait for Build & Test
- Hugging Face will automatically build your Space.
- Wait for the build to finish (watch the logs for errors).
- Test your app in the browser.
### 7. Troubleshooting
- If you see errors, check the build logs.
- Make sure all dependencies are in `requirements.txt`.
- Ensure your model path in `app.py` matches the uploaded file location.
### 8. Share Your Space
- Once working, share your Space URL (e.g., `https://huggingface.co/spaces/YOUR_USERNAME/acceptin`).
---
## 🏗️ Technical Overview
- **Model**: ConvNeXt Large (197M parameters, transfer learning from food detection)
- **Task**: Binary classification (good/bad telecom site)
- **App**: Streamlit web interface
- **Data**: Images of telecom sites, labeled as good or bad
- **Deployment**: Hugging Face Spaces (Streamlit SDK)
## 📋 Features
- Upload telecom site images for instant quality assessment
- Visual confidence scores and inspection breakdown
- Modern, responsive UI
## 📊 Model Performance
- **Validation Accuracy**: ~94%
- **Model Size**: ~750MB
## 📚 Data Requirements
- Images of telecom sites (good/bad)
- Recommended: 100+ images per class
---
**For more details, see the in-app documentation or contact the author.** |