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  • Adventures in PyTorch – Image classification with CalTech . . .
    The Dataset – The CalTech UCSD 200 Birds Database (CUB-200–2011) Figure 5: Example birds of similar species (warblers) from the CUB-200–2011, showing the complexity and difficulty of bird species identification Even with the species diagrams shown in Figure 6, it is difficult even for humans to predict the specific bird species
  • The eBird Taxonomy - Help Center
    Species Our species and subspecies taxonomy uses the Clements Checklist, a global bird taxonomy which follows regional authorities In the Americas, the Clements Checklist largely defers to the two AOS committees–the North American Classification Committee (NACC) and the South American Classification Committee (SACC)–with the goal of near-complete compliance
  • October Big Day results—more than 6,000 species in . . . - eBird
    On 6 October, more than 17,000 birders around the world went birding together for the first October Big Day Reporting from 146 countries, they tallied 6,136 species of birds: more than half of the world's birds in a single day
  • October Big Day results—more than 6,000 species in . . . - eBird
    On 6 October, more than 17,000 birders around the world went birding together for the first October Big Day Reporting from 146 countries, they tallied 6,136 species of birds: more than half of the world's birds in a single day
  • 40 Birds of Prey Found in the USA! (2025) - Bird Watching HQ
    What kinds of birds of prey can you find in the United States? This question is common, both for birders and non-birders alike Raptors are popular animals that tend to catch people’s interest more than most other species Luckily, there are many different species in all sorts of habitats 40 Birds of Prey IN THE UNITED STATES:
  • Bird Species Identification using Deep Learning
    identify the bird species more than audio classification Also, human ability to recognize the birds through the images is more understandable So this method uses the Caltech-UCSD Birds 200 [CUB-200-2011] dataset for training as well as testing purpose By using deep convolutional neural network
  • Bird species tolerance to human pressures and associations . . .
    This suggests that the distribution of Conservative HTI values across the ~6000 bird species may be driven more by the spatial distribution of human pressures than sampling bias We found non-random variation in bird species tolerances to human pressures across categories of IUCN population trend and Red List threat status





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