Identify Indian birds through sight and song.
BirdLens brings image and audio inference into one calm, product-quality interface for species recognition that feels precise, credible, and field-ready.
2
Detection modalities
Indian
Birdlife focus
Deep Learning
Core research approach
Field Session Preview
A refined detection experience, not a dashboard.
Indian Roller
Plumage contrast, head profile, and perched stance produced the strongest visual agreement.
Asian Koel
Phrase repetition and tonal rise aligned with the dominant acoustic signature.
Crafted Flow
Choose modality.
Upload the cleanest cue.
Review a concise, confidence-led shortlist.
Detection Modes
Choose the kind of field evidence you have.
BirdLens is structured around two distinct entry points so the experience stays focused, elegant, and intuitive from the first interaction.
Vision Model
Image Detection
Upload a bird photograph to identify likely Indian species through plumage, silhouette, posture, and visible field marks.
Best for perched birds, field photos, and clear silhouette-led sightings.
Acoustic Model
Audio Detection
Analyze calls and songs from field recordings using the updated LightBirdNet acoustic workflow.
Best for dawn choruses, hidden birds, and call-driven identification with ranked confidence.
How It Works
A short, deliberate path from input to answer.
The interface keeps the workflow calm and legible: upload once, review a refined result, and move forward with confidence.
01
Choose a modality
Begin with either a bird photo or a field recording, depending on what evidence you captured in the moment.
02
Submit the strongest cue
Provide the clearest crop or clip available so the model can focus on species-level traits rather than noise.
03
Review the shortlist
BirdLens returns a primary prediction, confidence estimate, and a refined set of nearby alternatives.
Why Multimodal Detection
Bird identification is rarely neat. The product should respect that.
BirdLens is built for real field conditions where some observations begin with a photograph, others with a call, and many with imperfect evidence that still deserves a thoughtful result experience.
Some sightings are visual first
Rich plumage, wing shape, crest, beak structure, and habitat context are often enough to narrow a species quickly.
Others are acoustic first
Dense foliage, dawn movement, and fast flyovers often reveal themselves through song or call before a photograph is possible.
BirdLens supports both field realities
The platform is organized around the way birding actually happens: partial evidence, shifting light, distant calls, and fast decisions.
Supported Detection Modes
Refined outputs, grounded interactions.
Each mode keeps the experience restrained and readable so results feel credible, not noisy.
Mode Highlight
Image-led identification
For field photos, mobile captures, and documented sightings.
Optimized for species cues like plumage contrast, silhouette geometry, and visible head markings.
Mode Highlight
Audio-led identification
For recordings of calls, songs, and ambient bird vocalizations.
Designed to surface top-ranked species from 5-second log-Mel spectrogram structure and vocal patterns.
Mode Highlight
Human-in-the-loop review
Results are presented as a refined decision aid, not a black-box verdict.
Confidence, alternative species, and a clear result layout help keep interpretation grounded.
About BirdLens
A flagship interface for a serious bird detection project.
The platform combines deep-learning experimentation with a product-quality frontend so bird species detection feels trustworthy, elegant, and ready to evolve beyond a notebook-only workflow.
Explore Detection Flow2
Detection modalities
Indian
Birdlife focus
Deep Learning
Core research approach
The current frontend uses carefully designed placeholder inference states until a live prediction API is connected. The interaction model, layout system, and result presentation are ready for production integration.