iWildCam Wildlife Detection & Classification
Camera trap image classification for wildlife monitoring (Beery et al., CVPR 2019). Classify wildlife species from camera trap images across diverse ecosystems worldwide.
image annotation
Configuration Fileconfig.yaml
# iWildCam Wildlife Detection & Classification Configuration
# Based on Beery et al., CVPR 2019
annotation_task_name: "iWildCam Wildlife Classification"
data_files:
- "sample-data.json"
item_properties:
id_key: "id"
text_key: "image_url"
context_key: "location_info"
user_config:
allow_all_users: true
annotation_schemes:
- annotation_type: "radio"
name: "primary_species"
description: "Select the primary species visible"
labels:
- name: "empty"
tooltip: "No animal visible"
- name: "deer"
tooltip: "Deer species"
- name: "wild_boar"
tooltip: "Wild boar/pig"
- name: "raccoon"
tooltip: "Raccoon"
- name: "fox"
tooltip: "Fox"
- name: "coyote"
tooltip: "Coyote"
- name: "bear"
tooltip: "Bear"
- name: "mountain_lion"
tooltip: "Mountain lion/cougar"
- name: "rabbit"
tooltip: "Rabbit/hare"
- name: "squirrel"
tooltip: "Squirrel"
- name: "bird"
tooltip: "Bird species"
- name: "cattle"
tooltip: "Cattle"
- name: "domestic_dog"
tooltip: "Domestic dog"
- name: "domestic_cat"
tooltip: "Domestic cat"
- name: "human"
tooltip: "Human"
- name: "other"
tooltip: "Other species"
- annotation_type: "radio"
name: "animal_count"
description: "How many animals are visible?"
labels:
- name: "zero"
tooltip: "No animals"
- name: "one"
tooltip: "Single animal"
- name: "few"
tooltip: "2-5 animals"
- name: "many"
tooltip: "More than 5 animals"
- annotation_type: "radio"
name: "image_quality"
description: "Image quality"
labels:
- name: "clear"
tooltip: "Clear, well-exposed"
- name: "blurry"
tooltip: "Motion blur"
- name: "dark"
tooltip: "Underexposed/dark"
- name: "overexposed"
tooltip: "Overexposed/washed out"
- name: "partial"
tooltip: "Animal partially visible"
- annotation_type: "multiselect"
name: "behavior"
description: "Select observed behaviors (if animal present)"
labels:
- name: "walking"
tooltip: "Walking or moving"
- name: "running"
tooltip: "Running"
- name: "standing"
tooltip: "Standing still"
- name: "feeding"
tooltip: "Eating/foraging"
- name: "resting"
tooltip: "Lying down/resting"
- name: "looking_at_camera"
tooltip: "Looking at camera"
- annotation_type: "text"
name: "specific_species"
description: "Enter specific species name if known"
interface_config:
item_display_format: "<img src='{{text}}' style='max-width:100%; max-height:500px;'/><br/><small>Location: {{context}}</small>"
output_annotation_format: "json"
output_annotation_dir: "annotations"
Sample Datasample-data.json
[
{
"id": "wild_001",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/1/18/Dog_Breeds.jpg/1200px-Dog_Breeds.jpg",
"location_info": "Camera trap image from North American forest. Identify species, count, and behavior."
},
{
"id": "wild_002",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg",
"location_info": "Wildlife camera capture. Classify the animal species visible."
}
]
// ... and 1 more itemsGet This Design
Clone or download from the repository
Quick start:
git clone https://github.com/davidjurgens/potato-showcase.git cd potato-showcase/iwildcam potato start config.yaml
Details
Annotation Types
Domain
Use Cases
Tags
Found an issue or want to improve this design?
Open an IssueRelated Designs
CUB-200-2011 Fine-Grained Bird Classification
Fine-grained visual categorization of 200 bird species (Wah et al., 2011). Annotate bird images with species labels, part locations, and attribute annotations.
DeepFashion Fine-Grained Fashion Classification
Large-scale fashion dataset with attribute prediction, consumer-to-shop matching, and landmark detection (Liu et al., CVPR 2016). Annotate clothing with 1000 categories and 50 attributes.
Open Images V6 Object Detection
Large-scale object detection following Open Images V6 (Kuznetsova et al., IJCV 2020). Annotate 600 object classes with bounding boxes, visual relationships, and instance segmentation masks.