BirdWatch v0.4.0 Released

The version 0.4.0 of BirdWatch – the Deep Learning Bird Image Identification System – was released today with several major improvements. The system now supports identification of 312 bird species (see the list of newly added birds below). The new and improved AI model we used in this version also shows significant improvements to the accuracy of the model in the dataset used: with a 98% top-1 accuracy and a 99% top-5 accuracy. This gives you the ability to identify large number of birds more accurately.

The new model was also upgraded to use TensorFlow 2.1, with Python 3.7. We tweaked the data augmentation configuration on the training data to achieve the higer accuracy. You can find more technical details at the The AI Behind BirdWatch page. The accuracy and loss graphs for training and fine-tuning phases of the model is shown beloe:

The Accuracy and Loss Graph for v0.4.0 Model Training Phase
The Accuracy and Loss Graph for v0.4.0 Model Training Phase
The Accuracy and Loss Graph for v0.4.0 Model Fine-tuning Phase

Following are the new birds added to the system:

Bird IDBird Name
1Black-headed Munia
2Black-naped Monarch
3Black-naped Tern
4Black-necked Stork
5Black-rumped Flameback
6Black-tailed Godwit
7Black-throated Munia
8Black-winged Kite
9Blue Rock Thrush
10Blue-eared Kingfisher
11Blue-faced Malkoha
13Common Buzzard
14Common Coot
15Common Greenshank
16Common Hawk Cuckoo
17Common Hoopoe
18Common Iora
19Common Kestrel
20Common Moorhen
21Common Myna
22Common Pochard
23Common Quail
24Common Redshank
25Common Ringed Plover
26Common Sandpiper
27Common Snipe
28Common Tailorbird
29Common Tern
30Common Woodshrike
31European Nightjar
32Great Bittern
33Great Crested Tern
34Great Frigatebird
35Great Grey Shrike
36Great Knot
37Great Thick-knee
38Great Tit
39Great White Pelican
40Indian Nightjar

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