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WolfVue: Wildlife Video Classifier

A tool for automatically classifying trail camera photos and videos by species using YOLO object detection, originally developed for The Gray Wolf Research Project. This project's main priority is for Idaho species commonly found on trailcams.

The 4-Tool Pipeline (run in this order)

Tool	What it does	One command
AnnotationTool	Smart resume annotation + auto-rename by species	python tools/annotationtool.py
WolfRank	Finds the folders with the most wolves in seconds	python tools/wolf_rank.py
WolfForge	Trains a custom YOLO wolf detector	python tools/wolf_forge.py
WolfVue	Real-time inference + video sorting (main app)	python WolfVue.py

All tools share the same WlfCamData.yaml - zero configuration hell.

---

Quick Start (literally 3 commands)

# 1. Clone + install
git clone https://github.com/ParadigmPacket/WolfVue.git
cd WolfVue
pip install ultralytics opencv-python colorama tqdm pyyaml

# 2. Annotate (resume from last image!)
python tools/annotationtool.py

# 3. Find the best wolf folders across 100 directories
python tools/wolf_rank.py

# 4. Train your own wolf detector
python tools/wolf_forge.py --epochs 100

# 5. Sort videos with the trained model
python WolfVue.py

That's it. No Roboflow. No paid APIs.

---

Features That Actually Matter in the Field

• Resume annotation from exact last image (no re-labeling 8,000 photos)
• Smart filename renaming using existing YOLO labels
• Automatic GPU batch sizing & early stopping
• ONNX/TensorRT export for edge devices (Raspberry Pi, Jetson, trail-cam PCs)
• Live TensorBoard + beautiful terminal UI
• Works offline in the Idaho backcountry

---

Quick Start

Prerequisites

Python 3.8 or higher installed on your system.

Download & Setup

Step 1: Download WolfVue

1. Click the green "Code" button at the top of this GitHub page
2. Select "Download ZIP"
3. Extract the ZIP file to your Desktop or Documents folder
4. Open the extracted folder - it should contain wolfvue.py, best.pt, and folders named input_videos/ and output_videos/

Step 2: Install Python

1. Download Python 3.8+ from python.org
2. During installation, CHECK "Add Python to PATH" (important!)
3. Restart your computer

Step 3: Install Required Packages

1. Open Command Prompt (Windows) or Terminal (Mac/Linux)
2. Navigate to your WolfVue folder:

   cd Desktop/WolfVue
   

3. Install packages:

   py -m pip install -r requirements.txt
   
   

Using WolfVue

Step 4: Add Your Videos and Photos

• Copy your trail camera videos into the input_videos/ folder
• Supported formats: .mp4, .avi, .mov, .mkv and most photo types.

Step 5: Run the Script

1. Open Command Prompt/Terminal and navigate to your WolfVue folder
2. Run:

   python WolfVue.py
   

   if this doesnt work, try clicking on the WolfVue folder, and then click "copy filepath". Then modify the original prompt.

Example:

python "C:\Users\Coastal_wolf\Desktop\WolfVue\WolfVue.py"

3. Press Enter for each prompt (unless you want to change file paths)
4. Wait for processing to complete - you'll see lots of scrolling text showing frame detection

Step 6: Check Results Find your sorted videos in:

• output_videos/Sorted/ - organized by species
• output_videos/Unsorted/ - mixed species or unclear
• output_videos/No_Animal/ - no animals detected
• processing_report.txt - detailed classification report

Troubleshooting

"Python is not recognized"

• Reinstall Python and check "Add Python to PATH"
• Restart your computer

"No module named 'ultralytics'"

• Make sure you ran the pip install command in the correct WolfVue folder

"No videos found"

• Check that videos are in the input_videos/ folder with supported file extensions

Script crashes

• Ensure best.pt and WlfCamData.yaml are in your WolfVue folder

Tips

• Test with 1-2 videos first
• Processing time varies by computer speed and video length
• Check the processing report for detailed explanations
• Yaml files for each model are often found paired with the .pt file in the weight's folder. Need help? Open an issue on GitHub with your error message and operating system.

How It Works

WolfVue processes trail camera footage frame by frame, detecting animals using a trained YOLO model. It then analyzes the temporal patterns of detections to classify each video into one of three categories:

• Species folders: Videos with a clear dominant species (>70% of detections)
• Unsorted: Videos with multiple species, predator-prey conflicts, or unclear patterns to be manually sorted
• No_Animal: Videos with zero animal detections

Configuration

The main parameters you can adjust in the script:

CONFIDENCE_THRESHOLD = 0.25          # Minimum YOLO confidence score
DOMINANT_SPECIES_THRESHOLD = 0.7     # Required percentage for dominant species
MAX_SPECIES_TRANSITIONS = 5          # Maximum allowed species changes
CONSECUTIVE_EMPTY_FRAMES = 30        # Empty frames to break a detection sequence

NOTE: this only adjusts the sorting algoritm based on frame detection BY the yolo model, but does not effect the YOLO model itself.

Advanced Usage

Understanding the Classification Algorithm

The classifier uses a multi-factor approach to determine video categories:

1. Detection Aggregation: All YOLO detections above the confidence threshold are collected frame by frame
2. Temporal Clustering: Consecutive frames with the same species are grouped into clusters
3. Transition Analysis: The algorithm counts how often the detected species changes throughout the video
4. Dominance Calculation: Both total detection count and frame coverage are considered

Classification Rules

A video is classified as a specific species only if:

• That species represents >70% of all detections
• Species transitions are below the threshold
• No predator-prey conflicts exist (e.g., wolf and deer in same video)

This conservative approach ensures high confidence in single-species classifications.

Model Requirements

The YOLO model (best.pt) must be trained to detect the species defined in WlfCamData.yaml. The yaml file maps class IDs to species names:

names:
  0: "WhiteTail"
  1: "MuleDeer"
  2: "Elk"
  3: "Moose"
  4: "Cougar"
  5: "Lynx"
  6: "Wolf"
  7: "Coyote"
  8: "Fox"
  9: "Bear"

Ideally, this list will expand in the future. It could have already changed, so refer the the actualy YAML file for updated pathing.

About YOLO Model WolfVue_Beta1

this model is a very rough model that was scraped together with as much data as I could find;

Coyote: 65 instances Elk: 236 instances Moose: 40 instances MuleDeer: 167 instances WhiteTail: 60 instances Wolf: 27 instances

this means its only actually able to identify 6 different species, is unbalanced, and highly skewed towards Elk because they make up so much of the dataset.

This is NOT a good model, but its a start.

I cannot share the data, as its restricted by the Gray Wolf Research Project, so open weight is the best I can do.

The goal of open sourcing this is to hopefully get some more trail cam videos that can be fine tuned for more species, more accurately, and maybe more efficiently. If im being completely honest I hardly know what im doing, so someone who does know what theyre doing might be able to take this to the next level, and make a good model that researchers and hobbiests may be able to utilize in the future.

you can find more details as to the results of this model training in its respective folder.

About YOLO Model WolfVue_Beta_BroadV2

This (Idaho specific) model represents a significant step forward from the original WolfVue_Beta1, expanding from 6 species to 11 different wildlife species. The model was trained on 4,338 images containing 2,625 individual animal annotations across 50 epochs, achieving an mAP50 score of 0.673. In simple terms, this means the model correctly identifies and locates animals about 67% of the time when tested on completely new images it has never seen before.

The dataset follows a standard 70/20/10 split, meaning 70% of the data was used for training the model, 20% for validation during training, and the final 10% was held back exclusively for testing accuracy. This testing portion ensures we get an honest assessment of how well the model performs on truly unseen data.

While the species count has nearly doubled, the dataset still suffers from significant imbalance issues. WhiteTail deer dominate the dataset with 908 annotations representing over a third of all data, while species like Fox and Cougar have only 19 and 18 samples respectively. This creates a balance ratio of 50:1 between the most and least common species, which is far from ideal. The model will naturally be much better at identifying common species like WhiteTail, Elk, and Cow, while struggling with the rare species that lack sufficient training examples.

Despite these limitations, this model can now identify Elk, WhiteTail deer, MuleDeer, Coyote, Cow, Black Bear, Rabbit, Moose, Wolf, Fox, and Cougar. The 67% accuracy represents solid progress, mostly dragged down by the under-represented species, though there's clearly room for improvement, especially for the underrepresented species.

I still cannot share MOST of the raw data due to restrictions with the Gray Wolf Research Project, so open weights remain the best contribution I can make. THis model does have a large portion of open data, largely annotated by me. I would estimate around 1500 ish annotations are made on free data I sourced from Idaho Fish and Game, so if youre interested in using these annotations, feel free to contact me at [email protected]. I would include them here, but adding images to this github repo is a nightmare.

you can also find most of the public trail-camera data (non annotated) here: https://lila.science/datasets/idaho-camera-traps/

SPLIT OVERVIEW: Split Images Files Annotations Percentage

train 3022 1511 1843 70.2 % val 858 429 500 19.0 % test 458 229 282 10.7 %

SPECIES DISTRIBUTION BREAKDOWN:

Species ID Train Val Test Total %

WhiteTail 0 634 181 93 908 34.6 % Elk 2 376 79 52 507 19.3 % Cow 10 228 64 32 324 12.3 % Wolf 6 159 50 27 236 9.0 % MuleDeer 1 155 50 28 233 8.9 % Fox 8 86 22 14 122 4.6 % Black Bear 9 65 18 11 94 3.6 % Moose 3 59 15 9 83 3.2 % Lynx 5 56 15 10 81 3.1 % Coyote 7 13 3 3 19 0.7 % Cougar 4 12 3 3 18 0.7 %

DATASET BALANCE ANALYSIS: Most common species: 908 annotations Least common species: 18 annotations Balance ratio: 50.4:1

about YOLO model WolfVue_LimitedV2

This model is more stable and accurate than any of my previous models, but more limited than WolfVue_Beta_BroadV2 in the number of species it can identify. Instead of having a large dataset with unbalanced training data, I focused on species that had 100+ annotations for stability. Each of the following species contained 133-250 annotations in the dataset across 6 common species, leading to a balanced model with little bias.

Species in model:

WhiteTail MuleDeer Elk Cow Black Bear Mule Deer

During training, this model achieved a 97.14% mAP50 score (accuracy).

Doing some real world testing, I picked a trail camera from Idaho Fish and Game with unseen data at random.

Loc_205 (avalible at https://lila.science/datasets/idaho-camera-traps/), contained mostly Elk. Testing on this real world dataset;

it correctly identified 92.95% (488 out of 525) of all species in the "sorted" folder output. It correctly identified No_animal 97.91% of the time (329 out of 329) It correctly sorted 90.93% (471 out of 518) of animals into the "Sorted" output folder

I believe for a folder containing mostly these common species, this model may actually be a viable automated solution with minimal human oversight. This model is a step in the right direction, and with time and more annotations, hopefully we can get similar results with 13 species or more.

Note: WolfVue automatically changes the thumbnail of the video to a point where an animal was detected. This makes it very easy to determine mistakes at a glance.

note about tools

these tools are mostly self-explanatory and (somewhat) easy to understand/operate when you run them but sometimes they have a few quirks I will note here.

for the annotation tool, if youre analyzing a new dataset, BE SURE that you load the correct yaml file or else it will say you have annotations of species you did not make e.g "395 Grizzly Bear annotations" because when you re-do the yaml you assign new number identifiers to the annotations, so you might get confusing results if your yaml doesnt correspond to you balanced dataset.

Performance Considerations

Processing speed depends on:

• Video resolution (higher resolution = slower)
• Model complexity (YOLOv8n is fastest, YOLOv8x is most accurate)
• Hardware (GPU acceleration dramatically improves speed)

For GPU acceleration, ensure you have CUDA-enabled PyTorch:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Batch Processing

The script automatically processes all videos in the input folder. For large datasets:

• The pre-scan estimates total processing time
• Progress bars show both per-video and overall progress
• A processing report is generated with detailed results

Output Structure

Videos are organized into a taxonomy-based folder structure:

output_videos/
├── Sorted/
│   ├── Ungulates/
│   │   ├── WhiteTail/
│   │   ├── MuleDeer/
│   │   ├── Elk/
│   │   └── Moose/
│   └── Predators/
│       ├── Cougar/
│       ├── Lynx/
│       ├── Wolf/
│       ├── Coyote/
│       ├── Fox/
│       └── Bear/
├── Unsorted/
└── No_Animal/

Customizing the Taxonomy

To modify the folder structure, edit the TAXONOMY dictionary in the script:

TAXONOMY = {
    "Category": {
        "Species": ["Species"],
        # Add more as needed
    }
}

Processing Report

After processing, check processing_report.txt for:

• Classification summary statistics
• Per-video classification details
• Detection rates and species percentages
• Reasoning for each classification decision

Troubleshooting

No videos found: Ensure videos are in common formats (.mp4, .avi, .mov, .mkv) and check both uppercase and lowercase extensions.

Path errors: The script uses relative paths from its location. Keep all folders (input_videos, output_videos, weights) in the same directory as the script.

Memory issues: For very long videos, consider splitting them or reducing resolution before processing.

Slow processing: Without GPU acceleration, expect approximately 10 frames per second on modern CPUs.

Technical Details

Frame-by-Frame Analysis

Each frame undergoes:

1. YOLO inference to detect bounding boxes
2. Confidence filtering
3. Species identification mapping
4. Temporal context integration

Temporal Consistency

The algorithm maintains temporal consistency by:

• Tracking species across consecutive frames
• Identifying detection clusters
• Penalizing frequent species transitions
• Handling gaps in detections (animal temporarily out of frame)

Edge Cases

The classifier handles several edge cases:

• Brief appearances by secondary species are ignored if under threshold
• Predator-prey scenarios always result in "Unsorted" classification
• Videos with sparse, intermittent detections are evaluated based on total pattern

final remarks

I want to preface this by saying most of this was created with AI, the scripting, learning how to train models, etc. While I have a basic understanding of python, I would not have been Able to achieve this without Claude, that said, developers will probably encounter some odd quirks in the code because of this, and I apologize in advance.

At first I was conflicted about using AI to code, but ultimately, the means that this is done does not matter so long as the end project benefits scientists. researchers, and hobbiests free of charge as intended.

Also note that I would like this project to be specifically for Trail cameras, so please make sure any data that is fine tuned is done with data FROM trail cameras.

Thank you for reading, and possibly using this. I think this could make for a great open source project!

What to improve on for the future

1. First and foremost, we need to improve the yolo model. Its pretty clear thats the main issue to be worked on.

I think focusing on large and common mammals from North America is important. I think we should catogorize things like birds broadly, as it would be a nightmare to try to identify each species, same with waterfowl. I think we should limit our scope to maybe 20 species at most for now if we get that many. We'll burn that bridge when we get to it i suppose. (also add more models which can be changed between within the program like a liberary)

2. Implement support for images.

Im already doing things frame by frame, this should be a no-brainer. It also should be incredibly easy. I just need to make the script recognize when images are input and sort those in the same way.

3. Add documentation for fine-tuning YOLO models

I will do this once ive re-learned how to do this myself

potentially underwater?

Potential feature ideas

1. figure out how to do things like automating animal size and color calculations, determine if possible and how to implement.

2. Research a potential option for image segmentation, might have to use a different model and make it an option, as it will be slower

Training new models

VERY valuable resource for idaho trail cameras: https://lila.science/datasets/idaho-camera-traps/

Contributing

When contributing, please maintain the existing code structure and add appropriate error handling for new features. The codebase prioritizes readability and maintainability over premature optimization.

Credits

Created by Nathan Bluto
initial data from The Gray Wolf Research Project
Facilitated by Dr. Ausband

License

GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007

Copyright (C) 2007 Free Software Foundation, Inc. https://fsf.org/ Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

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A "User Product" is either (1) a "consumer product", which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, "normally used" refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.

"Installation Information" for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.

If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).

The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.

Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.

7. Additional Terms.

"Additional permissions" are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.

When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.

Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:

a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or

b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or

c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or

d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or

e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or

f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.

All other non-permissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.

If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.

Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.

8. Termination.

You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).

However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.

Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.

Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.

9. Acceptance Not Required for Having Copies.

You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.

10. Automatic Licensing of Downstream Recipients.

Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.

An "entity transaction" is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.

You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.

11. Patents.

A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version".

A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.

Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.

In the following three paragraphs, a "patent license" is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To "grant" such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.

If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.

If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.

A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.

Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

12. No Surrender of Others' Freedom.

If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

13. Use with the GNU Affero General Public License.

Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

14. Revised Versions of this License.

The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.

Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.

If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.

Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.

15. Disclaimer of Warranty.

THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

16. Limitation of Liability.

IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

17. Interpretation of Sections 15 and 16.

If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.

                 END OF TERMS AND CONDITIONS

        How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.

<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.

The hypothetical commands show w' and show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box".

You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see https://www.gnu.org/licenses/.

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read https://www.gnu.org/licenses/why-not-lgpl.html.