adaptshot

v0.1.0 suspicious
5.0
Medium Risk

Human-aligned few-shot vision learning for resource-constrained environments

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious intent such as network calls, shell executions, or credential risks. However, the low metadata quality and moderate obfuscation risk raise concerns about its true purpose and origin.

  • Low metadata quality
  • Moderate obfuscation risk
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution detected, indicating the package does not execute system commands, reducing the risk of malicious activities.
  • Obfuscation: The observed patterns seem to be related to model evaluation and do not indicate malicious obfuscation but could suggest some level of code complexity or obscurity.
  • Credentials: No evidence of credential harvesting or secret handling was found.
  • Metadata: The package is newly created and lacks detailed author information, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • o(config.device) backbone.eval() # Preprocess image preprocess = _get_preprocess_t
  • ls)) self._model_head.eval() self._label_to_idx = label_to_idx self._id
  • """ self.model.eval() fisher: Dict[str, torch.Tensor] = {}
  • ss.item() self.model.eval() logger.info(f"CA-EWC Finetuning complete after {se
  • ) self.model.eval() """UP-UGF: Uncertainty-Guided Forgetting for bounded repla
  • t_embedding() correctly sets .eval() assert isinstance(model, torch.nn.Module), f"Facto
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository johnson2006christopher/adaptshot appears legitimate

⚠ Maintainer History score 6.0

3 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)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with adaptshot 
Create a small-scale wildlife identification app using the 'adaptshot' package. This application will enable users to take photos of animals and receive real-time identification results based on a few examples provided by the user. The app should be designed to work effectively even when there is limited data available for training models, making it ideal for remote or less explored areas where data collection might be challenging.

Step-by-Step Instructions:
1. Set up your development environment with Python and install the 'adaptshot' package.
2. Design a simple user interface (UI) where users can upload images or take pictures directly from their device.
3. Integrate the 'adaptshot' package to process the uploaded images and identify the animal species within them.
4. Implement a feature that allows users to provide feedback on the identification results, helping to refine future identifications.
5. Ensure the application can operate efficiently even with minimal computational resources, leveraging the 'adaptshot' package's ability to perform well with few-shot learning.
6. Add a database to store user-provided images and feedback, which can be used to improve model accuracy over time.
7. Incorporate a map feature showing locations where different species have been identified.
8. Test the application thoroughly to ensure it works as expected under various conditions.

Suggested Features:
- Real-time image processing and classification
- User feedback system to improve model performance
- Efficient operation in low-resource environments
- Database for storing images and feedback
- Map integration for visualizing species sightings

Utilization of 'adaptshot':
- Use 'adaptshot' for the core image classification task, taking advantage of its few-shot learning capabilities to accurately identify animal species with minimal training data.
- Leverage 'adaptshot' to enhance the application's performance in resource-constrained settings, ensuring it remains effective even when computational resources are limited.