Update README.md

README.md

@@ -19,4 +19,42 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+tags:
+- biology
+- microscopy
+pretty_name: Dreambooth Brightfield Microscopy
+size_categories:
+- 1K<n<10K
 ---
+# Dataset Card for Dreambooth Brightfield Microscopy
+
+This dataset was created as part of my masters research and thesis, where I am trying to generate realistic looking
+brightfield microscopy images for dataset augmentation.
+With the downstream goal of enhancing cell detection objects, increasing the dataset size of an object detection model
+is a necessary step.
+
+## Dataset Details
+
+### Dataset Description
+
+As part of my research, I previously generated brightfield microscopy images using unconditional diffusion models, curating a
+large dataset of SCC brightfield images.
+The results of these models were pretty impressive, but still needed many images.
+Hence, this dataset was created to test the capabilities of dreambooth on brightfield microscopy image generation.
+I'm testing several configurations:
+- Diffusion Model Architectures (SD-1.5, SD-2.1, SDXL 1.0)
+- Training Data Size (10, 20, 30, 50)
+- 4 Concepts are trained in parallel (cell, cell rug, well edge, debris)
+- With and without subject class images for class-specific prior preservation loss impact assessment
+
+The dataset consists of several classes:
+- Real microscopy images, one class for each concept
+- Generated images from SD-1.5, one class for each concept
+- Generated images from SD-2.1, one class for each concept
+- Generated images from SDXL 1.0, one class for each concept
+
+These classes are used in the concepts for the dreambooth model training,
+resulting in 24 models trained to assess the usability of dreambooth in this domain.
+Unfortunately, due to time constraints, I'm not able to test many hyperparameter configurations for each model, nor play around
+a lot with prompt engineering.
+This research serves as a base others (or me) can work upon.