=Paper=
{{Paper
|id=Vol-3180/paper-183
|storemode=property
|title=Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF
2022
|pdfUrl=https://ceur-ws.org/Vol-3180/paper-183.pdf
|volume=Vol-3180
|authors=Cheng Zou,Furong Xu,Meng Wang,Wen Li,Yuan Cheng
|dblpUrl=https://dblp.org/rec/conf/clef/ZouXWLC22
}}
==Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF
2022==
https://ceur-ws.org/Vol-3180/paper-183.pdf
Solutions for Fine-grained and Long-tailed Snake
Species Recognition in SnakeCLEF 2022
Cheng Zou1 , Furong Xu1 , Meng Wang1 , Wen Li1 and Yuan Cheng1
1
Ant Group, Yuan Space 556 Xixi Road, Hangzhou, 310013, China
Abstract
Automatic snake species recognition is important because it has vast potential to help lower deaths and
disabilities caused by snakebites. We introduce our solution in SnakeCLEF 2022 for fine-grained snake
species recognition on a heavy long-tailed class distribution. First, a network architecture is designed to
extract and fuse features from multiple modalities, i.e. photograph from visual modality and geographic
locality information from language modality. Then, logit adjustment based methods are studied to relieve
the impact caused by the severe class imbalance. Next, a combination of supervised and self-supervised
learning method is proposed to make full use of the dataset, including both labeled training data and
unlabeled testing data. Finally, post processing strategies, such as multi-scale and multi-crop test-time-
augmentation, location filtering and model ensemble, are employed for better performance. With an
ensemble of several different models, a private score 82.65%, ranking the 3rd, is achieved on the final
leaderboard.
Keywords
Snake Species Classification, Fine-grained Classification, Long-tailed Class Distribution, Self-supervised
Pretraining, SnakeCLEF
1. Introduction
Snakebite is a global health problem, especially in remote geographic areas and developing
countries. According to [1], in Asia, up to two million people are envenomed by snakes each year,
while in Africa, there are about 435,000 to 580,000 snakebites annually that need treatment, for
they can cause permanent disability and disfigurement. Taxonomic knowledge about snakes is
crucial in diagnosis and medical response to snakebites, and accurate identification of the snake
species is important for the appropriate treatment of snakebite victims since specific antivenoms
are effective against specific venomous snakes [2]. Manual identification, e.g. training doctors
on each species, is no easy feat, because there are more than 3,500 species of snakes, 600 of
which are venomous [3]. So, building an automatic and robust image-based system for snake
species identification has the greatest potential to save lives [4].
The difficulty of snake species identification, from both a human and a machine perspective,
lies in the high intra-class and low inter-class variance in appearance, which may depend
on geographic location, colour morph, sex, or age [4]. Sometimes, having the image alone is
not enough, because many species are visually similar to other species, while introducing the
CLEF 2022: Conference and Labs of the Evaluation Forum, September 5β8, 2022, Bologna, Italy
$ wuyou.zc@antgroup.com (C. Zou); booyoungxu.xfr@antgroup.com (F. Xu); darren.wm@antgroup.com
(M. Wang); yinian.lw@alibaba-inc.com (W. Li); chengyuan.c@antgroup.com (Y. Cheng)
Β© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
geographic origin of an observation can help to recognize considerably. The task of SnakeCLEF
2022 challenge [5], as part of the LifeCLEF 2022 [6, 7], aims to recognize a snake species ID
given multiple photographs of the same individual and its corresponding geographic locality
information.
In this paper, we introduce the solution of team "SAI" in SnakeCLEF 2022 for fine-grained
snake species recognition on a severe long-tailed class distribution. First, as discussed above,
using the image data alone is not enough, more cues are required for prediction, so a network
architecture is designed to extract and fuse features from multiple modalities, i.e. photograph
from visual modality and geographic locality information from language modality. Then,
because of the heavy long-tailed class distribution, some logit adjustment based methods are
studied to relieve the impact caused by the severe class imbalance, which significantly improves
the performance. Next, to make full use of the dataset, including both labeled training data
and unlabeled testing data, a combination of supervised and self-supervised learning method
is utilized for pretraining. Finally, some post processing strategies are employed for better
performance, including multi-scale and multi-crop test-time-augmentation, location filtering
and model ensemble.
2. Related Work
2.1. Fine-grained Vision Classification
Snake species recognition is basically a task of fine-grained vision classification (FGVC). Modern
fine-grained image classification methods can be divided into two parts, use image data only [8,
9, 10, 11], and use image data as well as extra data from other modalities [12, 13, 14, 15]. For
the task of snake species recognition, one can solve it by using image data only, but a better
choice is to use both image data and geographic locality information. In such studies with
extra modalities, [12] is a classic method to introduce geographic information, [13] introduced
spatio-temporal information into the network. MetaFormer [15] proposed a unified and flexibly
framework to joint the visual appearance and various meta-information.
2.2. Long-tailed Distribution
Snake species recognition in real world is also a task of long-tailed image classification. Recently,
long-tailed learning has received plenty of research interests, and among which there are mainly
two kinds of solutions, one is post-hoc normalisation of the classification weights [16, 17, 18, 19],
and the other is modification of the loss to account for varying class penalties [20, 21, 22].
[23] revisited the classic idea of logit adjustment based on the label frequencies, either applied
post-hoc to a trained model, or enforced in the loss during training.
2.3. Snake Species Classification
Before us, there have been a few works tried to build automatic snake species recognition
systems. In [1, 24], object detectors were first trained to reduce clutter and drop the unnecessary
background for preprocessing, and then the detected snakes were classified by trained deep
Figure 1: Overall network architecture. It has a hybrid framework, where CNN branch is used to extract
vision features, MLP branch is used to encode meta data, and transformer is used to fuse vision features
and meta information. π(π₯) stands for extracted features and π (π₯) stands for the predicted logits.
models. [25] extracted feature for each image with Inception ResNet V2 and concatenated it
with geographic feature, then the concatenated features were classified using a lightweight
gradient boost classifier. In [24], EfficientNet and Vision Transformer were trained, and the
prior probabilities of location information were multiplied with the model predictions in a
subsequent step. Besides, [2, 3, 24, 26] used multiple models to improve the performance.
3. Methodology
3.1. Overview
The proposed solution for fine-grained snake species recognition on a long-tailed class dis-
tribution mainly consists of four parts: 1) a network architecture to extract and fuse features
from multiple modalities, 2) logit adjustment to relieve the impact caused by the severe class
imbalance. 3) a combination of supervised and self-supervised learning method to make full
use of both labeled training data and unlabeled testing data, 4) post processing strategies such
as multi-scale and multi-crop test-time-augmentation, location filtering and model ensemble.
3.2. Network Architecture
The network design follows MetaFormer [15]. It has a hybrid framework, where on one branch
CNN is used to extract vision features, on the other branch MLP is used to encode meta data,
and transformer is later used to fuse vision features and meta information. The output feature
map of CNN branch is transformed to a series of patch embeddings (denoted as patch tokens),
along with the output embedding of MLP branch (denoted as meta token), along with the class
token, are fed into the transformer layers for prediction. In the task of snake species recognition,
the meta data available consists of location code, country and the tag of endemic.
3.3. Logit Adjustment for Long-tailed Learning
According to statistics, the dataset has a heavy long-tailed class distribution, where the most
frequent species is represented by 6,472 images and the least frequent species by just 5 samples.
It is also noteworthy that the evaluation metric, the Mean (Macro) F1-Score, weights recall
and precision equally, and a good retrieval algorithm will maximize both precision and recall
simultaneously. Thus, moderately good performance on both will be favoured over extremely
good performance on one and poor performance on the other. If there is no action to deal with
long-tailed distribution problem, the recall for rare classes would be very low, thus the overall
performance could not be high.
Logit adjustment [23] is adopted to relieve the long-tailed distribution problem. In post-hoc
adjustment, we predict the following instead of the original one,
argmaxπ¦β[πΏ] exp(π (π₯)π¦ )/ππ¦π = argmaxπ¦β[πΏ] π (π₯)π¦ β π Β· log ππ¦ (1)
In logit adjusted loss, it is combined with the soft target cross entropy loss,
πΏ(π (π₯)π¦ , π¦) = βπ¦ Β· log softmax(π (π₯)π¦ + π Β· log ππ¦ ) (2)
where π > 0 is a hyper-parameter, π (π₯)π¦ is the output logits of the neural network given
input π₯, and ππ¦ is the estimate of the class prior, e.g., the empirical class frequency on the
training data. In practice, one can use either post-hoc logit adjustment or logit adjusted loss.
3.4. Combination of Supervised and Self-supervised Learning
In order to make full use of the dataset, including both labeled training data and unlabeled
testing data, a combination of supervised and self-supervised training framework is proposed
for pretraining. Specifically, we perform supervised learning on labelled training data and self-
supervised learning (SSL) on all the data to obtain a set of task-related parameter initialization.
Since the task provides images and meta information, we do pretraining for MetaFormer [15]
with meta data, which can jointly take advantage of vision and meta-information.
To obtain a task-relevant initialization instead of imagenet initialization, we combine the
self-supervised method SimCLR [27] with MetaFormer. Specifically, for each input pair of image
and meta data, we randomly perform two data augmentations (Fig. 2) for the image, but no
augmentation on meta data, then a classification loss SoftTargetCE [28, 29] (short for soft target
cross entropy) is applied to the labeled data only, and a contrastive loss InfoNCE [30] is applied
to all the data. Thus, the loss function for pretraining is,
Β― ]), π )
πΏππππ‘ππππ (π, π ) = SoftTargetCEπ ΜΈ=β1 (π (π), π ) + πΌ Β· InfoNCE(π([π; π (3)
where π (π) stands for logits, π(π) stands for extracted features for a given batch π, and π
stands for its corresponding label, π = β1 means unlabeled testing data. π Β― is an augmented
version of π. πΌ is a hyper-parameter to balance the relative importance between supervised
loss and self-supervised loss.
Algorithm 1: Location filtering
Input : logits_adjusted, locations-to-species mapping πΏ2π, meta data π ππ‘π
Output : predicted species name
1 idx_sort = np.argsort(logits_adjusted)[::-1]
2 for idx in idx_sort do
3 species_name = species_list[idx]
4 if species_name in L2S[Meta[βcodeβ]] then
5 break
6 return species_name
3.5. Post Processing
Two kinds of post processing strategies are mainly used, one is multi-scale, multi-crop and
multi-model ensemble, the other is location filtering. For model ensemble, average logits is first
calculated based on the output logits of different models, then the mean logits is adjusted to
deal with long-tailed distribution problem. Specifically, for each single scale/crop input π₯π , the
πβth model outputs logits ππ (π₯π ), then the final logits used for prediction is,
βοΈ βοΈ
logits_adjusted = mean( ππ (π₯π )) β π Β· log π (4)
π π
Location Filtering. During training, it can be found that top-5 accuracy is much higher than
that of top-1, which implies that if properly chosen from top predictions, the result could be
better than the naive argmax one. A locations-to-species mapping is used to heuristically choose
the best candidate prediction. For simplicity, we iterate through a sorted logits list until the
first certain species appears, whose species name co-occurs with its location code. Algorithm 1
shows the numpy style pseudo code.
4. Experiments
4.1. Experimental Settings
Dataset: The dataset is based on 187,129 snake observations with 318,532 photographs belonging
to 1,572 snake species and observed in 208 countries. The data were gathered from the online
biodiversity platform iNaturalist. The provided dataset has a heavy long-tailed class distribution,
where the most frequent species (Natrix natrix) is represented by 6,472 images and the least
frequent species by just 5 samples.
Evaluation Metric: The evaluation metric for this competition is macro πΉ1 -Score. The πΉ1
score for the π-th species is computed as,
ππππππ ππππ Β· πππππππ
πΉ1π = 2 Β· (5)
ππππππ ππππ + πππππππ
Figure 2: Augmentations for self-supervised learning. From left to right, top to bottom: original image
without augmentation, color jittering, gray scale, horizontal flip, random resized crop, Gaussian blur,
random erasing, a composed random augmentation of the above.
The macro πΉ1 is calculated by averaging the πΉ1 scores over all the species [1],
π
βοΈ πΉπ
macro πΉ1 = 1
(6)
π
π=1
where π is the number of species. The macro πΉ1 score is not biased by class frequencies and is
more suitable for the long-tailed class distributions. The πΉ1 metric weights recall and precision
equally, and a good retrieval algorithm will maximize both precision and recall simultaneously.
Thus, moderately good performance on both will be favoured over extremely good performance
on one and poor performance on the other.
Implementation Details: MetaFormer [15] is selected as our base network. More specifically,
we use MetaFormer-2 with extra meta information as input for both pretraining and finetuning.
The meta data available for snake species consists of location code, country and the tag of
endemic. Thus we construct a 2438-d vector to encode all of the above meta data, and then
send it to an MLP to generate meta token. Hyper-parameter π in Eq. 1 is set to 0.55, πΌ in Eq. 3
is set to 0.001. In supervised and self-supervised pretraining, ImageNet-21k pretrained model
is loaded as initialization, and the learning rate is initialized as 5 Γ 10β5 . In later finetuning,
the learning rate is initialized as 5 Γ 10β6 . The models are trained on 8-NVIDIA A100-GPU
machines for 300 epochs with a per GPU batch size of 72 for size 384 and 32 for size 512. The
augmentations for self-supervised learning is illustrated in Fig. 2, while those for supervised
learning and finetuning still follows MetaFormer.
Table 1
Representative experimental results about the importance of: 1) meta information, 2) different pretrained
models, 3) solving long-tailed class distribution problem, 4) location filtering, 5) different input image
size, 6) model ensemble. Itβs noteworthy that different parts in the table have different baselines, which
is mainly caused by limited number of submissions.
Experimental Settings macro πΉ1 Comments
The importance of meta information
Vision Only 66.64 % ImageNet-21k pretraining + Location filtering
Vision + Meta-208d 71.11 % ImageNet-21k pretraining + Location filtering
Vision + Meta-2438d 74.19 % ImageNet-21k pretraining + Location filtering
Different pretrained models
ImageNet-1k [15] 63.76 % Without location filtering or logit adjustment
ImageNet-21k [15] 66.64 % Without location filtering or logit adjustment
iNat21 [15] 64.32 % Without location filtering or logit adjustment
Supervised+SSL 68.83 % Without location filtering or logit adjustment
Solving long-tailed class distribution
None 74.19 % ImageNet-21k pretraining + Location filtering
Seesaw [31] 76.49 % ImageNet-21k pretraining + Location filtering
Logit adjusted loss [23] 78.01 % ImageNet-21k pretraining + Location filtering
Post-hoc logit adjustment [23] 78.57 % ImageNet-21k pretraining + Location filtering
Location filtering
Without 64.32 % iNat21 + No meta data + No logit adjustment
With 69.09 % iNat21 + No meta data + No logit adjustment
Different input image size
384 81.18 % Meta+Logit adjustment+SSL+Location filtering
512 82.03 % Meta+Logit adjustment+SSL+Location filtering
Model ensemble
Model1 81.18 % input image size 384
Model2 82.03 % input image size 512
Ensemble 82.72 %
4.2. Experimental Results
In this part, we report some representative experimental results, including: 1) the importance
of meta information, 2) the importance of pretrained models, 3) the importance of solving
long-tailed class distribution, 4) the importance of location filtering, 5) the importance of larger
input size, 6) the importance of model ensemble.
The experimental results are summarized in Tab. 1. Academically, these comparisons here
are not strict ablation studies, because they are obtained by few limited submissions during the
competition. However, these results provided a meaningful and effective path to optimize the
solution, which did improve the online judge performance.
As shown in Tab. 1, training the model with additional meta information significantly im-
proves the performance from 66.64% to 74.19%, which indicates the importance of data from
multiple modalities. For pretrained models, the proposed task-related supervised+SSL pretrain-
ing for Snake performs better than those commonly used ones, which indicates the importance
of using unlabeled testing data. In long-tailed learning, logit adjustment is proved to be more
effective, which improves the score from 74.19% to 78.57%. Location filtering, a task-specific
post processing, using the statistics prior from the whole dataset to remove illegal predictions,
improves the score from 64.32% to 69.09%. Also, training with larger input size 512 improves
the performance from 81.18% to 82.03%.
With an ensemble of seven different models, we got private score 82.65% on the final leader-
board. The improvement is marginal compared to a single model, because the differences among
these models are small, i.e., different epochs, different hyper-parameters. Interestingly, in late
submission, we find it inferior to 82.72%, the ensemble of only two models.
5. Conclusion and Future Work
In this paper, we introduce our solution in SnakeCLEF 2022 for fine-grained snake species
recognition on a severe long-tailed class distribution. Attentions have been mainly focused
on four parts: 1) fusion of vision features and meta information, 2) solving long-tailed class
distribution problem, 3) making full use of both the labeled training data and unlabeled testing
data via supervised and self-supervised pretraining, 4) post processing strategies such as location
filtering and model ensemble.
Though great improvements have been made, there still exist some actions of great potential
for future work: 1) hard example mining for fine-grained and long-tailed dataset, 2) treating
it as a retrieval task not a classification task, 3) using a snake detection model to get more
precise bounding box for data pre-processing. We have tried some of the above but none of
them contributed to the final performance during the competition, but they have great potential
if further studied.
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A. Online Resources
The code and models are available at Google Drive.