2017

iNaturalist Competition

Please open an issue if you have questions or problems with the dataset.

2017 Competition

The 2017 competition, sponsored by Google, is part of the FGVC^4 workshop at CVPR.

Updates

February 15th, 2021:

• AWS Open Data download links now freely available. See the Data section below.

August 17th, 2020:

• AWS S3 download links were created due to problems with the original Google and Caltech links. The dataset files are in a "requester pays" bucket, so you will need to download them through an AWS API. See the Data section below.

August 16th, 2019:

• Additional metadata in the form of latitude, longitude, date, and user_id for each of images in the train and validation sets can be found here.

Kaggle

We are using Kaggle to host the leaderboard. Checkout the competition page here.

Dates

Data Released	April 5, 2017
Submission Server Open	June 1, 2017
Submission Deadline	July 7, 2017
Winners Announced	July 21, 2017

Details

There are a total of 5,089 categories in the dataset, with 579,184 training images and 95,986 validation images. For the training set, the distribution of images per category follows the observation frequency of that category by the iNaturalist community. Therefore, there is a non-uniform distribution of images per category. Example images, along with their unique GBIF ID numbers (where available), can be viewed here.

Super Category	Category Count	Train Images	Val Images
Plantae	2,101	158,407	38,206
Insecta	1,021	100,479	18,076
Aves	964	214,295	21,226
Reptilia	289	35,201	5,680
Mammalia	186	29,333	3,490
Fungi	121	5,826	1,780
Amphibia	115	15,318	2,385
Mollusca	93	7,536	1,841
Animalia	77	5,228	1,362
Arachnida	56	4,873	1,086
Actinopterygii	53	1,982	637
Chromista	9	398	144
Protozoa	4	308	73
Total	5,089	579,184	95,986

Evaluation

We follow a similar metric to the classification tasks of the ILSVRC. For each image , an algorithm will produce 5 labels , . We allow 5 labels because some categories are disambiguated with additional data provided by the observer, such as latitude, longitude and date. It might also be the case that multiple categories occur in an image (e.g. a photo of a bee on a flower). For this competition each image has one ground truth label , and the error for that image is:

Where

The overall error score for an algorithm is the average error over all test images:

Guidelines

Participants are restricted to train their algorithms on iNaturalist 2017 train and validation sets. Pretrained models may be used to construct the algorithms (e.g. ImageNet pretrained models) as long as participants do not actively collect additional data for the target categories of the iNaturalist 2017 competition. Please specify any and all external data used for training when uploading results.

The general rule is that we want participants to use only the provided training and validation images to train a model to classify the test images. We do not want participants crawling the web in search of additional data for the target categories. Participants should be in the mindset that this is the only data available for those categories.

Participants are allowed to collect additional annotations (e.g. bounding boxes) on the provided training and validation sets. Teams should specify that they collected additional annotations when submitting results.

Annotation Format

We closely follow the annotation format of the COCO dataset. The annotations are stored in the JSON format and are organized as follows:

{
  "info" : info,
  "images" : [image],
  "categories" : [category],
  "annotations" : [annotation],
  "licenses" : [license]
}

info{
  "year" : int,
  "version" : str,
  "description" : str,
  "contributor" : str,
  "url" : str,
  "date_created" : datetime,
}

image{
  "id" : int,
  "width" : int,
  "height" : int,
  "file_name" : str,
  "license" : int,
  "rights_holder" : str
}

category{
  "id" : int,
  "name" : str,
  "supercategory" : str,
}

annotation{
  "id" : int,
  "image_id" : int,
  "category_id" : int
}

license{
  "id" : int,
  "name" : str,
  "url" : str
}

Submission Format

The submission format for the Kaggle competition is a csv file with the following format:

id,predicted
12345,0 78 23 3 42
67890,83 13 42 0 21

The id column corresponds to the test image id. The predicted column corresponds to 5 category ids, separated by spaces. You should have one row for each test image.

Bounding Boxes

A subset of the dataset has been annotated with bounding boxes, please see the paper for the full details on how the boxes were collected. The following super categories were annotated:

Super Category	Train Boxes	Val Boxes
Insecta	106,304	16,732
Aves	283,931	17,314
Reptilia	36,476	5,480
Mammalia	31,109	2,654
Amphibia	15,812	2,280
Mollusca	8,566	1,571
Animalia	6,643	1,143
Arachnida	4,752	1,051
Actinopterygii	2,571	511
Total	496,164	48,736

The bounding box format follows the COCO format:

annotation{
  "id" : int,
  "image_id" : int,
  "category_id" : int,
  "bbox" : [x, y, width, height],
  "area" : float,
  "iscrowd" : 0
}

The bbox units are in pixels, the origin is the upper left hand corner, and the area value is approximated as (width * height) / 2.0 since we did not collect segmentation masks.

Bounding Box Caveats:

• Crowdworkers were asked to annotate at the super category level rather than the category level. Therefore, for images with multiple box annotations there is no guarantee that all instances actually belong to the same category even though they are labeled as being the same category (e.g. multiple bird species could be boxed in an image and labeled as the same species). Rather, the boxed instances will belong to the same super category. Due to this problem, the validation set is restricted to images with single instances so that we can be confident the category label is correct for the given box.
• iscrowd is hard coded to 0 for all annotations, although it is possible that a box is around a crowd of instances (such as barnacles or mussels).

Terms of Use

By downloading this dataset you agree to the following terms:

1. You will abide by the iNaturalist Terms of Service
2. You will use the data only for non-commercial research and educational purposes.
3. You will NOT distribute the above images.
4. The California Institute of Technology makes no representations or warranties regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose.
5. You accept full responsibility for your use of the data and shall defend and indemnify the California Institute of Technology, including its employees, officers and agents, against any and all claims arising from your use of the data, including but not limited to your use of any copies of copyrighted images that you may create from the data.

Data

The dataset is freely available through the AWS Open Data Program. Download the dataset files here:

• Training and validation images [186GB]
  • s3://ml-inat-competition-datasets/2017/train_val_images.tar.gz
  • Running md5sum on the tar.gz file should produce 7c784ea5e424efaec655bd392f87301f train_val_images.tar.gz
  • Images have a max dimension of 800px and have been converted to JPEG format
  • Untaring the images creates a directory structure like train_val_images/super category/category/image.jpg. This may take a while.
• Training and validation annotations [26MB]
  • s3://ml-inat-competition-datasets/2017/train_val2017.zip
• Training bounding box annotations [22MB]
  • s3://ml-inat-competition-datasets/2017/train_2017_bboxes.zip
• Validation bounding box annotations [3MB]
  • s3://ml-inat-competition-datasets/2017/val_2017_bboxes.zip
• Location annotations (train and val) [12MB]
  • s3://ml-inat-competition-datasets/2017/inat2017_locations.zip
  • Running md5sum inat2017_locations.zip should produce afc1956f9a100b165b89f3923d040912
• Test images [53GB]
  • s3://ml-inat-competition-datasets/2017/test2017.tar.gz
  • Running md5sum on the tar.gz file should produce 7d9b096fa1cd94d67a0fa779ea301234 test2017.tar.gz
  • Images have a max dimension of 800px and have been converted to JPEG format
• Test image info [6.3MB]
  • s3://ml-inat-competition-datasets/2017/test2017.zip

Example s3cmd usage for downloading the training and validation images:

pip install s3cmd

s3cmd \
--access_key XXXXXXXXXXXXXXXXXXXX \
--secret_key XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX \
get s3://ml-inat-competition-datasets/2017/train_val_images.tar.gz .