amd-torchvision-device

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torchvision-device

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has no apparent malicious activities such as network calls or shell executions, but its metadata suggests a lack of proper development effort, making it suspicious.

  • Low metadata quality
  • Lack of maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal for most packages unless they require external services.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, reducing potential risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several signs of low-effort creation and lack of maintainer history, raising suspicion but not definitive proof of malice.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amd-torchvision-device 
Your task is to develop a real-time object detection application using the Python package 'amd-torchvision-device'. This application will leverage advanced GPU capabilities provided by AMD GPUs to perform efficient and fast object detection on live video streams.

### Project Overview:
- **Application Name:** Real-Time Object Detection Stream
- **Primary Functionality:** Detect objects in real-time from a live video stream and display bounding boxes around detected objects.
- **Target Audience:** Developers, hobbyists, and anyone interested in computer vision applications.

### Core Features:
1. **Live Video Capture:** Utilize a webcam or any other video input source to capture live video streams.
2. **Real-Time Object Detection:** Implement a model capable of detecting multiple types of objects in real-time. The model should be optimized for speed and accuracy using the 'amd-torchvision-device' package.
3. **Display Results:** Display the live video feed with bounding boxes around detected objects. Optionally, include labels and confidence scores for each detected object.
4. **User Interface:** Develop a simple UI that allows users to start/stop the video stream, switch between different models, and adjust detection parameters if necessary.
5. **Model Optimization:** Use the 'amd-torchvision-device' package to optimize the object detection model for performance on AMD GPUs. Ensure that the application takes full advantage of parallel processing capabilities.
6. **Error Handling:** Implement robust error handling to manage issues like loss of video input, model loading failures, etc.
7. **Documentation:** Provide comprehensive documentation detailing setup instructions, API usage, and troubleshooting tips.

### How to Utilize 'amd-torchvision-device':
- **Installation:** Begin by installing the 'amd-torchvision-device' package via pip.
- **Model Loading & Optimization:** Load a pre-trained object detection model and use 'amd-torchvision-device' to optimize it for AMD GPU acceleration.
- **Inference:** Perform inference on live video frames using the optimized model. Ensure that the application maintains high frame rates and low latency.
- **Performance Monitoring:** Optionally, include functionality to monitor the performance of the application, such as FPS (frames per second) and model inference times.

### Deliverables:
- A fully functional application that can run on a system equipped with an AMD GPU.
- Source code with comments and explanations.
- Comprehensive documentation on how to set up and run the application.
- Example videos or screenshots demonstrating the application's capabilities.

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