| --- |
| license: cc-by-4.0 |
| tags: |
| - vision |
| - object-detection |
| dataset_info: |
| - config_name: default |
| base_datasets: |
| - coco |
| --- |
| |
| ## Use Case |
|
|
| This dataset is useful for demonstrations of how to train tiny vision models. The size of the dataset was reduced from ~25GB to ~300MB/900MB (grayscale/color) for quick and easy installation. This dataset focuses of the binary task of detecting a person in the image (similar to Visual Wake Words) but still contains all labels (needs to be manually relabelled to use for binary task). |
|
|
|
|
| ## Dataset Description |
|
|
| This dataset is a derivative work based on the **Microsoft COCO (Common Objects in Context) Dataset** created by the COCO Consortium. It modifies the original dataset by [briefly describe your changes, |
| applying the preprocessing described below. |
|
|
|
|
| ## Preprocessing |
|
|
| 1. Official COCO 2017 dataset. |
| 2. Removal of all images tagged as "person" that do not have bounding boxes provided. |
| 3. Boundig-box-aware cropping of the images to 1:1 aspect ratio (prevents cutting out the target information). |
| 4. Downsampling to 64x64. |
| 5. Transformation to grayscale (pixel dtype = uint8). |
| 6. Removal of all iamges tagged as "person" that have small bounding boxes (area < 32x32 px). |
| 7. Compression with GZIP. |
|
|
| **NOTE:** The "easy" version removes all images tagged as "person" that have bounding boxes with area < `int(0.85*TARGET_SIZE)**2`. Also the images are downsampled to 32x32 for fast demonstrations. |
|
|
|
|
|
|
| ## Size |
| ``` |
| Total images in raw COCO dataset (train+test+val): 163,957 |
| Images discarded: 87,856 |
| Images remaining: 76,101 |
| ``` |
|
|
|
|
| ## How To Use |
| ````python |
| import os |
| import tensorflow as tf |
| from huggingface_hub import snapshot_download |
| |
| # Define the repository details |
| REPO_ID = "slamanigg/tiny-presence-detection" |
| |
| # Download into a local cache directory |
| repo_dir = snapshot_download(repo_id=REPO_ID, repo_type="dataset") |
| print(f"Repository files successfully downloaded to: {repo_dir}\n") |
| |
| # Pick a version and load |
| CHOSEN_VERSION = "tiny_presence_64_CH_1" |
| version_path = os.path.join(repo_dir, CHOSEN_VERSION) |
| loaded_ds = tf.data.Dataset.load(version_path,bcompression='GZIP') |
| print(loaded_ds) |
| ```` |
|
|
| ## Limitations |
|
|
| This dataset was exported in tensorflow (2.20.0) and can therefor only be loaded with tensorflow. |
|
|
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the following original COCO dataset papers: |
|
|
| ```bibtex |
| @inproceedings{Lin2014COCO, |
| author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence}, |
| title = {Microsoft COCO: Common Objects in Context}, |
| booktitle = {Computer Vision -- ECCV 2014}, |
| year = {2014}, |
| pages = {740--755}, |
| publisher = {Springer} |
| } |
| |
| @article{Chen2015COCOCaptions, |
| title = {Microsoft {COCO} Captions: Data Collection and Evaluation Server}, |
| author = {Chen, Xinlei and Fang, Hao and Lin, Tsung-Yi and Vedantam, Ramakrishna and Gupta, Saurabh and Doll{\'a}r, Piotr and Zitnick, C. Lawrence}, |
| journal = {arXiv preprint arXiv:1504.00325}, |
| year = {2015} |
| } |
