ran formatter on the codebase

Author: MechaCritter

.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 88
 ignore = E203, W503
-exclude = .git,__pycache__,venv, .gitignore
+exclude = .git,__pycache__,venv, .gitignore

.gitignore

@@ -177,4 +177,4 @@ res/
 
 # Files to keep in the ignored folders
 !res/images/
-!pyvisim/res/
+!pyvisim/res/

LICENSE

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

README.md

@@ -28,7 +28,7 @@ and the Siamese Neural Networks.
 
 ## Why `pyvisim`?
 
-`pyvisim` is designed to provide a simple and efficient way to compare images. 
+`pyvisim` is designed to provide a simple and efficient way to compare images.
 
 ### Quick Start
 
@@ -82,7 +82,7 @@ have any suggestions or questions!
 5. **Siamese Network (Coming Soon!)**  
    - Train a neural network to learn a similarity function directly from pairs/triples of images.  
    - Possible use cases include face recognition, signature verification, or any image-based identity matching.
-   
+
 ## Installation
 
 To use the library, you can simply install it via pip:
@@ -96,10 +96,10 @@ or clone the repository and install it locally:
 git clone https://github.com/MechaCritter/Python-Visual-Similarity.git
 cd Python-Visual-Similarity
 pip install .
-``` 
+```
 Note that the *notebooks are only available if you clone the repository.*
 
-All experiments in this project was made on the Oxford Flower Dataset <ref>[7]</ref>, for which I 
+All experiments in this project was made on the Oxford Flower Dataset <ref>[7]</ref>, for which I
 have created a custom dataset class. To use this class, import it as follows:
 
 ```python
@@ -109,9 +109,9 @@ For more details on the dataset, please refer to the [documentation](pyvisim/dat
 
 ## Pretrained Models
 
-The following pretrained models are provided for clustering and dimensionality reduction. All clustering 
-models were trained with `k=256`. The choice of `k` was made arbitrarily 
-based on the paper <sup>[5](#references)</sup>, where the authors tested with `k=32`, `64`, `128`, `256`, `512`, and so on. 
+The following pretrained models are provided for clustering and dimensionality reduction. All clustering
+models were trained with `k=256`. The choice of `k` was made arbitrarily
+based on the paper <sup>[5](#references)</sup>, where the authors tested with `k=32`, `64`, `128`, `256`, `512`, and so on.
 Since higher values would take too long, I chose `k=256` as a balance between performance and computational cost.
 
 ### KMeans Models
@@ -153,7 +153,7 @@ You can access these weights by importing `GMMWeights` from the `pyvisim.encoder
 
 ## Contributing
 
-We love contributions of all kinds—whether it’s suggesting new features, fixing bugs, or writing docs! Here’s how you 
+We love contributions of all kinds—whether it’s suggesting new features, fixing bugs, or writing docs! Here’s how you
 can get involved:
 
 1. **Fork** this repository.  
@@ -184,17 +184,16 @@ This project is licensed under the terms of the MIT license.
 
 ## References
 
-[1] Weixia Zhang, Jia Yan, Wenxuan Shi, Tianpeng Feng, and Dexiang Deng, "Refining Deep Convolutional Features for 
+[1] Weixia Zhang, Jia Yan, Wenxuan Shi, Tianpeng Feng, and Dexiang Deng, "Refining Deep Convolutional Features for
 Improving Fine-Grained Image Recognition," EURASIP Journal on Image and Video Processing, 2017. \
 [2] Relja Arandjelović and Andrew Zisserman, 'All About VLAD', Department of Engineering Science, University of Oxford. \
-[3] E. Spyromitros-Xioufis, S. Papadopoulos, I. Kompatsiaris, G. Tsoumakas, and I. Vlahavas, "An Empirical Study on the 
-Combination of SURF Features with VLAD Vectors for Image Search," Informatics and Telematics Institute, Center for Research and 
+[3] E. Spyromitros-Xioufis, S. Papadopoulos, I. Kompatsiaris, G. Tsoumakas, and I. Vlahavas, "An Empirical Study on the
+Combination of SURF Features with VLAD Vectors for Image Search," Informatics and Telematics Institute, Center for Research and
 Technology Hellas, Thessaloniki, Greece; Department of Informatics, Aristotle University of Thessaloniki, Greece. \
-[4] Relja Arandjelović and Andrew Zisserman, "Three things everyone should know to improve object retrieval," Department of   
+[4] Relja Arandjelović and Andrew Zisserman, "Three things everyone should know to improve object retrieval," Department of  
 Engineering Science, University of Oxford. \
-[5] Hervé Jégou, Florent Perronnin, Matthijs Douze, Jorge Sánchez, Patrick Pérez, and Cordelia Schmid, "Aggregating Local 
+[5] Hervé Jégou, Florent Perronnin, Matthijs Douze, Jorge Sánchez, Patrick Pérez, and Cordelia Schmid, "Aggregating Local
 Image Descriptors into Compact Codes," IEEE. \
-[6] Liangliang Wang and Deepu Rajan, "An Image Similarity Descriptor for Classification Tasks," J. Vis. Commun. 
+[6] Liangliang Wang and Deepu Rajan, "An Image Similarity Descriptor for Classification Tasks," J. Vis. Commun.
 Image R., vol. 71, pp. 102847, 2020. \
 [7] [Oxford Flower Dataset](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/).
-