Paper Citation Record · LEDGER

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers

As of 18 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.21364.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.21364 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:54:07.365473Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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verified fuzzy20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33ec7f13-0606-403c-aedd-a24abb04b80e · outbound

This paper cites Elephant poaching in south africa,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Elephant poaching in south africa,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:12.773230Z

Source-reported events for the cited work

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Observation ab8bc672-8e26-4419-a7a7-20580716ef7b · outbound

This paper cites Transfer learning for wildlife classification: Evaluating YOLOv8 against densenet, resnet, and vggnet on a custom dataset,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Transfer learning for wildlife classification: Evaluating YOLOv8 against densenet, resnet, and vggnet on a custom dataset,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:12.512565Z

Source-reported events for the cited work

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Observation 60d1a2d7-36e1-4d4e-910b-a48ed4207a49 · outbound

This paper cites Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:12.276223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11f5aef2-ac0e-4fb2-ab93-e6add216f47a · outbound

This paper cites Metadata augmented deep neural networks for wild animal classification,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Metadata augmented deep neural networks for wild animal classification,

Reference 4

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Observation 4d5440dd-28f5-4342-b04f-ef7d52cd48e7 · outbound

This paper cites A review of deep learning techniques for detecting animals in aerial and satellite images,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers A review of deep learning techniques for detecting animals in aerial and satellite images,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:12.098522Z

Source-reported events for the cited work

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Observation f3796e71-2030-4b27-8cd3-b406f0d2d02f · outbound

This paper cites African wildlife dataset,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers African wildlife dataset,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:11.868924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64227524-147d-4dfe-8d28-9b9454306af9 · outbound

This paper cites Densely connected convolutional networks,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Densely connected convolutional networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:11.552245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4bf123a7-9738-4d70-9b92-cc89c8b4ae0d · outbound

This paper cites Deep residual learning for image recognition,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Deep residual learning for image recognition,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:11.537943Z

Source-reported events for the cited work

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Observation 79df1cae-7ad9-4f7f-be44-61bd5f0d64f0 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Efficientnet: Rethinking model scaling for convolutional neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:11.310929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7ed0294-5371-495f-88ef-f1a773932cc6 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:11.129895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6f1a81d9-517b-4faf-8dd2-842b645f63a1 · outbound

This paper cites Data from: Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Data from: Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b5f82dcf-7263-4615-8263-c2fa94e07061 · outbound

This paper cites Recognition in terra incognita: Wildlife object classification in unseen domains,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Recognition in terra incognita: Wildlife object classification in unseen domains,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:10.649195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 585a5b78-1093-43ef-bb7b-d2103a09a14e · outbound

This paper cites The iWildCam 2018 Challenge Dataset.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers The iWildCam 2018 Challenge Dataset

Reference 13

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Source-reported events for the cited work

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Observation 7161f4ca-2845-46b9-9d78-737390e57ad3 · outbound

This paper cites WILDS: A benchmark of in-the-wild distribution shifts,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers WILDS: A benchmark of in-the-wild distribution shifts,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:10.355237Z

Source-reported events for the cited work

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Observation 4539a719-5bc0-4c8a-a8d4-481b23735d33 · outbound

This paper cites Automated wildlife image classification: An active learning tool for ecological applications,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Automated wildlife image classification: An active learning tool for ecological applications,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:10.094958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d903fb4-b6ab-4710-91c8-11a2805c3ac1 · outbound

This paper cites Animal species detection and classification framework based on modified multi- scale attention mechanism and feature pyramid network,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Animal species detection and classification framework based on modified multi- scale attention mechanism and feature pyramid network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:09.873897Z

Source-reported events for the cited work

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Observation 13c12439-2190-44c6-92ca-9cea6e7839df · outbound

This paper cites Advancements in Image Classification using Convolutional Neural Network.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Advancements in Image Classification using Convolutional Neural Network

Reference 17

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Observation 01940790-a6fc-47c0-a1bb-de5a5da4251b · outbound

This paper cites A survey on data augmentation for deep learning,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers A survey on data augmentation for deep learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:09.504008Z

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Observation 5cc5fb40-f98f-47a1-81f7-7140775bd8ce · outbound

This paper cites torchvision.models.vit h 14,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers torchvision.models.vit h 14,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:09.304285Z

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Observation c954df61-8ff9-47d1-924f-38892b654548 · outbound

This paper cites Densely connected convolutional networks,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Densely connected convolutional networks,

Reference 20

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Source-reported events for the cited work

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Observation ad30e73a-01eb-49b2-abd5-7edb534ade45 · outbound

This paper cites Models and pre-trained weights,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Models and pre-trained weights,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:09.026909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 489d8237-4e87-472e-abf4-a3b9731c21c2 · outbound

This paper cites Experiment tracking with weights and biases,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Experiment tracking with weights and biases,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:08.739580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 679737f3-37a3-40f3-a87f-874a5eb1800d · outbound

This paper cites Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:54:07.796907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 257b41ab-4320-4e6c-881b-51136bf631f2 · outbound

This paper cites Wildlife species classification on the edge: A deep learning perspective,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Wildlife species classification on the edge: A deep learning perspective,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:08.499559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 674ca138-6814-4e92-a7e1-0d8af40c7869 · outbound

This paper cites Living planet report 2022 – regional fact sheet: Africa,.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Living planet report 2022 – regional fact sheet: Africa,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:54:08.303856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e52bdab6-ec4b-4cd4-b355-b22bfb73832f · outbound

This paper cites Available: https://doi.org/10.5061/dryad.5pt92.

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Available: https://doi.org/10.5061/dryad.5pt92

Reference 2015

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.