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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: prompt |
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dtype: string |
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- name: class_name |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3307884193.56 |
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num_examples: 5560 |
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download_size: 2737134316 |
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dataset_size: 3307884193.56 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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tags: |
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- biology |
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- microscopy |
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- brightfield |
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pretty_name: Dreambooth Brightfield Microscopy |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- text-to-image |
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language: |
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- en |
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--- |
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# Dataset Card for Dreambooth Brightfield Microscopy |
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This dataset was created as part of my masters research and thesis, where I am trying to generate realistic looking |
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brightfield microscopy images for dataset augmentation. |
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With the downstream goal of enhancing cell detection objects, increasing the dataset size of an object detection model |
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is a necessary step. |
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## Dataset Details |
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### Dataset Description |
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As part of my research, I previously generated brightfield microscopy images using unconditional diffusion models, curating a |
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large dataset of SCC brightfield images. |
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The results of these models were pretty impressive, but still needed many images. |
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Hence, this dataset was created to test the capabilities of dreambooth on brightfield microscopy image generation. |
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I'm testing several configurations: |
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- Diffusion Model Architectures (SD-1.5(, SD-2.1, SDXL 1.0)) -- The last two had to be discontinued due to time, and compute constraints |
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- Training Data Size (10, 20, 30, 50) |
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- 4 Concepts are trained in parallel (cell, cell rug, well edge, debris) |
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- With and without subject class images for class-specific prior preservation loss impact assessment |
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The dataset consists of several classes: |
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- Real microscopy images, one class for each concept |
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- Generated images from SD-1.5, one class for each concept |
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- Generated images from SD-2.1, one class for each concept |
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- Generated images from SDXL 1.0, one class for each concept |
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These classes are used in the concepts for the dreambooth model training, |
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resulting in 8 models trained to assess the usability of dreambooth in this domain. |
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Unfortunately, due to time constraints, I'm not able to test many hyperparameter configurations for each model, nor play around |
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a lot with prompt engineering. |
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This research serves as a base thath others (or me) can work upon. |
