Duplicate from gwenxin/pills_inside_bottles

Co-authored-by: Wenxin Guo <gwenxin@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,55 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,128 @@
+---
+language:
+- en
+license: cc-by-4.0
+license_link: https://creativecommons.org/licenses/by/4.0/
+tags:
+- image-classification
+pretty_name: pills-inside-bottles
+size_categories:
+- "10K<n<100K"
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: ndc
+      dtype: string
+    - name: id
+      dtype: string
+splits:
+    - name: train
+      num_examples: 8393
+    - name: test
+      num_examples: 2786
+    - name: validation
+      num_examples: 2776
+download_size: 13955
+dataset_size: 13955
+
+---
+
+# Dataset Card for Pills Inside Bottles
+
+The dataset contains 13,955 imgaes of pills inside medication bottles, their National Drug Code (NDC), and image id.
+
+
+## Dataset Details
+
+### Dataset Description
+
+The dataset contains 13,955 imgaes of pills inside medication bottles, which are from a top down view. They are labeled with 20 distinct National Drug Code (NDC) and each image is associated with an image id. The dataset is split into train, test, and validation sets. 
+
+- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)
+
+### Dataset Sources
+
+- **Homepage:** [Images of pills inside medication bottles dataset](https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997#read_me_display)
+- **Paper:** [Performance evaluation of a prescription medication image classification model: an observational cohort](https://www.nature.com/articles/s41746-021-00483-8)
+
+
+## Uses
+
+### Direct Use
+
+The dataset can be used to train image classification models, which might be helpful for identifying pills automatically to increase efficiency and reduce dispensing error of pills in pharmacy.
+
+### Example Use Case
+
+Here is a link to an [example use case](https://colab.research.google.com/drive/1UPaAnVacx3ZpOy_koWFwWIluhVZYOdhT?usp=sharing) of this dataset, which trained supervised models to predict the national drug code for each image. The highest accuracy obtained is about 97% using EfficientNetV2M as the pre-train model and Support Vector Machines as the supervised learning algorithm. 
+
+
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the following features: _image_, _ndc_, and _id_.
+
+An example from the train set looks like the following:
+```
+{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1284x960>,
+ 'ndc': '29159',
+ 'id': '00378-3855'}
+```
+
+### Data Fields
+
+- 'image': a image of pills inside a medication bottle
+- 'ndc': National Drug Code
+- 'id': unique image id
+
+### Data Splits
+
+The dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of images as well as their corresponding NDCs and IDs. The following table contains the number of images in each split and the percentages. 
+
+| Dataset Split | Number of Instances in Split | Percent       |
+| ------------- | ---------------------------- | ------------- |
+| Train         | 8,393                        | 60.1%         |
+| Validation    | 2,786                        | 20.0%         |
+| Test          | 2,776                        | 19.9%         |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The data is collected for training image classification model to facillitate work in pharmacy.
+
+### Source Data
+
+#### Data Collection and Processing
+
+The data is collected by a group of researchers, including Lester, C. A., Al Kontar, R., and Chen, Q., whose paper, "Performance Evaluation of a Prescription Medication Image
+Classification Model: An Observational Cohort", is published in 2022.
+
+#### Who are the source data producers?
+
+According to Lester et. al, the dataset is produced by a commercial medication dispensing robot used at a mail-order pharmacy from a top-down view (Lester et. al, 6).
+
+## Bias, Risks, and Limitations
+
+The researchers of the original paper only released part of their data. Therefore, the model performance might be influenced negatively due to insufficient training data.
+
+
+## Citation
+
+**BibTeX:**
+```
+@InProceedings{University of Michigan - Deep Blue Data,
+title = {Images of pills inside medication bottles dataset},
+author={Lester, C. A., Al Kontar, R., Chen, Q.},
+year={2022}
+}
+```
+
+## More Information
+
+This dataset contain all information from the source data. The only change made is to rearrange the structure by extracting the file names, which correspond to NDC and image id, and put them into separated columns.
+

pills_inside_bottles.py

@@ -0,0 +1,98 @@
+
+"""The dataset contains 13,955 imgaes of pills inside medication bottles, 
+which are from a top down view. They are labeled with 20 distinct National 
+Drug Code (NDC) and each image is associated with an image id. The dataset
+ is split into train, test, and validation sets. """
+
+
+import os
+from typing import List
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{University of Michigan - Deep Blue Data,
+title = {Images of pills inside medication bottles dataset},
+author={Lester, C. A., Al Kontar, R., Chen, Q.},
+year={2022}
+}
+"""
+
+
+_DESCRIPTION = """\
+This dataset contains pills images inside medication bottles from a top down view, with National Drug Code (NDC) and image id.
+"""
+
+
+_HOMEPAGE = "https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997?locale=en#items_display"
+
+
+_LICENSE = "CC BY 4.0"
+
+
+_URLS = {
+    "dataset": "https://deepblue.lib.umich.edu/data/downloads/rr171x63c",
+}
+
+
+
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """The dataset contains train, test, and validation data for pills images inside medication bottles"""
+    _URLS = _URLS
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "id": datasets.Value("string"),
+                "ndc": datasets.Value("string")
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+
+        data_dir = dl_manager.download_and_extract(self._URLS["dataset"])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "NLM20/train")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "NLM20/test")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(data_dir, "NLM20/valid")},
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+
+        for ndc in os.listdir(filepath):
+            ndc_path = os.path.join(filepath, ndc)
+            if os.path.isdir(ndc_path):
+                for image_file in os.listdir(ndc_path):
+                    image_path = os.path.join(ndc_path, image_file)
+                    image_id = os.path.splitext(image_file)[0]
+                    yield image_id, {
+                        "image": image_path,
+                        "id": image_id,
+                        "ndc": ndc,
+                        }
+                
+                
+