In this large-scale training run I'll be using 5177 negative images, and 750 positive elephant-images. I'll attempt hard-negative mining on 500 images. Hard-negative mining is outlined in the following paper 'Object Detection with Discriminatively Trained Part Based Models' (Felzenszwalb et al.).

I'll be using the following EC2 instance: r4.16xlarge, with vCPU=64, ECU=195, and Memory GB=488. It's about $5/hr for rental. So hopefully I won't get broken pipe on the SSH session near the end!

Storage is 300GiB with provisioned IOPS SSD, 15000 IOPS. So that's extra cost I expect!

Negative images to use:

• chimps = 110
• bears = 102
• blimps = 86
• bonsai = 122
• bulldozers = 110
• cactus = 114
• camel = 110
• canoe = 104
• covered wagon = 97
• cormorant = 106
• dog = 103
• duck = 87
• elk = 101
• fern = 110
• firetruck = 118
• giraffe = 84
• goat = 112
• goose = 110
• gorilla = 212
• horse = 270
• ibis = 120
• kangaroo = 82
• leopards = 190
• llama = 119
• ostrich = 109
• owl = 120
• palm tree = 105
• people = 209
• porcupine = 101
• raccoon = 140
• skunk = 81
• snake = 112
• swan = 115
• touring bike = 110
• car side view = 116
• zebra = 96
• greyhound = 95
• toads = 108
• rhinos (own dataset) = 95
• [4591 total running]
• India cows (own dataset) = 41
• Sloth Bears (own dataset) = 45
• buffalo = 250
• tigers (2) = 250