Use this skill when you need to process a collection of camera trap images, run species classification, extract vector representation embeddings, and store them inside a Hoplite vector database.
Workflow Overview
Follow these sequential steps:
- Identify Dataset and Limits:
- Locate the target camera trap images directory.
- Assess if a GPU is available on the system. If running on CPU-only, discuss with the user or apply a processing limit (e.g., first 1000 images) to prevent the ingestion pipeline from running excessively long.
- Initialize Hoplite Database:
- Create a Hoplite database (
SQLiteUSearchDB) at the destination folder. - Configure it with an embedding dimension of
1280(EfficientNet-V2 M feature size), the metric set toCos, and the data type set tofloat16.
- Create a Hoplite database (
- Run Ingestion Pipeline:
- Instantiate the
SpeciesNetDetectorandSpeciesNetClassifiermodels (if running in an environment with pre-mounted read-only models like/kaggle/input/on Colab, copy the model directory to a local writable path first; see the Technical Reference). - Register a PyTorch forward hook on the classifier's average pooling
layer (
SpeciesNet/efficientnetv2-m/avg_pool/Mean_Squeeze__3825) to intercept raw embeddings. - For each image:
- Insert it into the database as a recording.
- Run the detector model to get bounding box coords for animal detections.
- Crop the PIL image to the bounding box, preprocess it, and run the classifier to extract the 1280-dim embedding vector.
- Cast the vector to
float16and insert it into the database as a window.
- Commit database changes periodically and at the end of ingestion via
db.commit()to persist SQLite rows and flush the USearch vector index to disk.
- Instantiate the
- Agile Modeling and Search:
- Once populated, use the Hoplite database to perform vector searches (ranking by similarity) or train active learning classifiers on top of the embeddings.
- Camera Trap Visualization Guidelines (M3 UI):
- Context Preservation: Avoid displaying raw cropped images in the
result cards. Instead, display the original (uncropped) image inside the
card container and draw the animal detection as a red border box overlay
dynamically using CSS absolute positioning and percentages (e.g.,
left: xmin * 100%,top: ymin * 100%, etc.). - Full-Resolution Modal Preview: Implement a click handler on the card media that triggers a floating fullscreen modal containing the uncropped image and the aligned bounding box overlay to allow the user to verify low-confidence detections.
- Custom Query Search Support: The backend server supporting the Web UI must implement on-the-fly embedding extraction for custom HTTP/S query URIs by downloading the image, running the detector to identify target bounding boxes, preprocessing the crop, and capturing the embedding vector using the PyTorch forward hook on the classifier.
- Context Preservation: Avoid displaying raw cropped images in the
result cards. Instead, display the original (uncropped) image inside the
card container and draw the animal detection as a red border box overlay
dynamically using CSS absolute positioning and percentages (e.g.,
Technical Reference
For detailed model loading code, Kaggle/Colab read-only filesystem workarounds, PyTorch hook embedding extraction, and bounding box calculations, see: