Image-based identification of plant species
Note: Do not forget to read the Rules section on this page
Automated identification of plants and animals has improved considerably in the last few years. In the scope of LifeCLEF 2017 in particular, we measured impressive identification performance achieved thanks to recent deep learning models (e.g. up to 90% classification accuracy over 10K species). This raises the question of how far automated systems are from the human expertise and of whether there is a upper bound that can not be exceeded. A picture actually contains only a partial information about the observed plant and it is often not sufficient to determine the right species with certainty. For instance, a decisive organ such as the flower or the fruit, might not be visible at the time a plant was observed. Or some of the discriminant patterns might be very hard or unlikely to be observed in a picture such as the presence of pills or latex, or the morphology of the root. As a consequence, even the best experts can be confused and/or disagree between each others when attempting to identify a plant from a set of pictures. Similar issues arise for most living organisms including fishes, birds, insects, etc. Quantifying this intrinsic data uncertainty and comparing it to the performance of the best automated systems is of high interest for both computer scientists and expert naturalists.
The goal of the task will be to return the most likely species for each observation of the test set. 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
The small fraction of the test set identified by the pool of experts will then be used to conduct the experts vs. machines evaluation.
To conduct a valuable experts vs. machines experiment, we collected image-based identifications from the best experts in the plant domain. Therefore, we created sets of observations that were identified in the field by other experts (in order to have a near-perfect golden standard). These pictures will be immersed in a much larger test set that will have to be processed by the participating systems. As for training data, the datasets of the previous LifeCLEF campaigns will be made available to the participants and might be extended with new contents. It will contain between 1M and 2M pictures.
As soon as the submission is open, you will find a “Create Submission” button on this page (just next to the tabs)
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.
LifeCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2018. CLEF 2018 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 2017 working notes (task overviews and participant working notes) can be found within CLEF 2017 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2018. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:
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
LifeCLEF 2018 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.
- Technical issues : https://gitter.im/crowdAI/lifeclef-2018-expert
- Discussion Forum : https://www.crowdai.org/challenges/lifeclef-2018-expert/topics
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 :
- Sharada Prasanna Mohanty: email@example.com
- Hervé Goëau: herve[DOT]goeau[AT]cirad[DOT]fr
- Alexis Joly: alexis[DOT]joly[AT]inria[DOT]fr
- Ivan Eggel: ivan[DOT]eggel[AT]hevs[DOT]ch
You can find additional information on the challenge here: http://imageclef.org/node/231