764 research outputs found
The iWildCam 2019 Challenge Dataset
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data?
In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment
Large-Scale Plant Classification with Deep Neural Networks
This paper discusses the potential of applying deep learning techniques for
plant classification and its usage for citizen science in large-scale
biodiversity monitoring. We show that plant classification using near
state-of-the-art convolutional network architectures like ResNet50 achieves
significant improvements in accuracy compared to the most widespread plant
classification application in test sets composed of thousands of different
species labels. We find that the predictions can be confidently used as a
baseline classification in citizen science communities like iNaturalist (or its
Spanish fork, Natusfera) which in turn can share their data with biodiversity
portals like GBIF.Comment: 5 pages, 3 figures, 1 table. Published at Proocedings of ACM
Computing Frontiers Conference 201
The iNaturalist Species Classification and Detection Dataset
Existing image classification datasets used in computer vision tend to have a
uniform distribution of images across object categories. In contrast, the
natural world is heavily imbalanced, as some species are more abundant and
easier to photograph than others. To encourage further progress in challenging
real world conditions we present the iNaturalist species classification and
detection dataset, consisting of 859,000 images from over 5,000 different
species of plants and animals. It features visually similar species, captured
in a wide variety of situations, from all over the world. Images were collected
with different camera types, have varying image quality, feature a large class
imbalance, and have been verified by multiple citizen scientists. We discuss
the collection of the dataset and present extensive baseline experiments using
state-of-the-art computer vision classification and detection models. Results
show that current non-ensemble based methods achieve only 67% top one
classification accuracy, illustrating the difficulty of the dataset.
Specifically, we observe poor results for classes with small numbers of
training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201
The iWildCam 2019 Challenge Dataset
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data?
In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Pre-training has been widely adopted in deep learning to improve model
performance, especially when the training data for a target task is limited. In
our work, we seek to understand the implications of this training strategy on
the generalization properties of downstream models. More specifically, we ask
the following question: how do properties of the pre-training distribution
affect the robustness of a fine-tuned model? The properties we explore include
the label space, label semantics, image diversity, data domains, and data
quantity of the pre-training distribution. We find that the primary factor
influencing downstream effective robustness (Taori et al., 2020) is data
quantity, while other factors have limited significance. For example, reducing
the number of ImageNet pre-training classes by 4x while increasing the number
of images per class by 4x (that is, keeping total data quantity fixed) does not
impact the robustness of fine-tuned models. We demonstrate our findings on
pre-training distributions drawn from various natural and synthetic data
sources, primarily using the iWildCam-WILDS distribution shift as a test for
downstream robustness
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