Update README.md

README.md

@@ -4,3 +4,12 @@ This dataset consists of data scraped from Bing images using an iCrawler bot. Ad
 **Pipeline diagram**
 Below you can see a diagram of the entire pipeline for gathering this data
 ![Processing pipeline diagram](BingRecycleDatasetDiagram.png)
+
+**Github Repositories**
+If you would like to use the same workflow I used, here is each repository listed below:
+[iCrawler scraper](https://github.com/real6c/RecycleImageDatasetWebScraper): Scrapes images from web using queries.txt, contains script to remove duplicates
+[Remove watermark banners from bottom of images](https://github.com/real6c/auto-banner-cropper): This will crop the image until it finds a sharp color difference using greyscale filters with darkness values
+[Watermark mask generation](https://github.com/real6c/yolo-watermark-detection): Generate the masks for watermarks with YOLO detect inference and OWLv2 for quick screening
+[Remove watermarks](https://github.com/real6c/IOPaintDataset): Adapted from the IOPaint pip package, allows user to use CLI (iopaint run) with large datasets (recursive directories and batching), uses LAMA model
+[Ollama VLM Screening](https://github.com/real6c/vlm-dataset-filtering): VLM classification of images based off different criteria with local Ollama server, determines which images are salvageable or should be removed
+[Final YOLO cls conversion](https://github.com/real6c/yolo-cls-dataset-converter): This converts the dataset into a YOLO classify format, and uses the JSON outputs from the above step to determine to keep or discard image