Loading
Cookies help us deliver our services. By using our services, you agree to our use of cookies. Learn more

LifeCLEF 2019 Plant

Image-based plant identification on Amazonian flora


43 days left
0
Submissions
99
Participants
1934
Views

Overview

Note: Do not forget to read the Rules section on this page

Usage scenario

Automated identification of plants has improved considerably in the last few years. In the scope of LifeCLEF 2017 and 2018 in particular, we measured impressive identification performance over 10K species. However, these 10K species, mostly living in Europe and North America, only represent the tip of the iceberg. The vast majority of the species in the world (~369K species) actually lives in data deficient regions and the performance of state-of-the-art machine learning algorithms on these species is unknown and presumably much lower because of the weak amount of training data. Thus, the main focus of the 2019 edition of PlantCLEF will be to evaluate automated identification on the flora of such data deficient regions.

Challenge description

he goal of the task is return the most likely species for each observation of the test set (an observation being a set of images of the same individual plant and the associated metadata such as date, gps, author). A small part of the observations in the test set will be re-annotated by several experts so as to allow comparing the performance of the evaluated systems with the one of highly skilled experts.

Data

We provide a new dataset of 10K species mainly focused on the Guiana shield and the Amazon rainforest (known to be the largest collection of living plants and animal species in the world). The average number of images per species in that new dataset will be much lower than the dataset used in the previous editions of PlantCLEF (about 10 vs. 100). Many species will contain only a few images and some of them might even contain only 1 image.

The training data is now available (see the “Dataset” tab). The test set to be predicted will be delivered around the 1st of March 2019.

Participants are allowed to use complementary training data (e.g. for pre-training purposes) but at the condition that (i) the experiment is entirely re-produceable, i.e. that the used external resource is clearly referenced and accessible to any other research group in the world, (ii) the use of external training data or not is mentioned for each run, and (iii) the additional resource does not contain any of the test observations.

Submission instructions

More practically, the run file to be submitted has to contain as much lines as the number of predictions, each prediction being composed of an ObservationId (the identifier of a specimen that can be itself composed of several images), a ClassId, a Probability and a Rank (used in case of equal probabilities). Each line should have the following format: <ObservationId;ClassId;Probability;Rank>

Here is a short fake run example respecting this format for only 3 observations: fake_run

As soon as the submission is open, you will find a “Create Submission” button on this page (just next to the tabs).

Comparison with PlantCLEF 2018

Before the opening of the PlantCLEF 2019 submissions, we encourage participants to train their system and submit runs to the challenge of last year (dealing with 10K species of data-abundant regions). We therefore re-open a new submission round and leaderboard on the challenge page: ExpertCLEF2018. Note that the best performing models of last year have been shared by CVUT at the following URL: http://ptak.felk.cvut.cz/personal/sulcmila/models/LifeCLEF2018/

Evaluation

The two main evaluation metrics will be the top-1 accuracy on 1) the fraction of the test set identified by the pool of experts, 2) on the whole test set.

Rules

LifeCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2019. CLEF 2019 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here .

Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by LifeCLEF organizing committee to ensure quality. As an illustration, LifeCLEF 2018 working notes (task overviews and participant working notes) can be found within CLEF 2018 CEUR-WS proceedings.

Important

Participants of this challenge will automatically be registered at CLEF 2019. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:

  • First name

  • Last name

  • Affiliation

  • Address

  • City

  • Country

  • Regarding the username, please choose a name that represents your team.

This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs

Prizes

LifeCLEF 2019 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.

Resources

Pre-trained plant identification models (from ExpertLifeCLEF 2018)

In order to support research in fine-grained plant classification, CVUT shares the pre-trained Inception-v4 and Inception-ResNet-v2 CNN models from their winning submission to the ExpertLifeCLEF 2018 Plant identification task. The pre-trained models may be a good starting point for the participants to LifeCLEF 2019: http://ptak.felk.cvut.cz/personal/sulcmila/models/LifeCLEF2018/

Contact us

We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at lifeclef-org[AT]inria[DOT]fr

More information

You can find additional information on the challenge here: https://www.imageclef.org/PlantCLEF2019