Update README.md

README.md

@@ -49,4 +49,76 @@ tags:
 pretty_name: COCO2017-Colorization
 size_categories:
 - 100K<n<1M
----
+---
+# COCO 2017 Dataset for Image Colorization
+
+## Overview
+
+This dataset is derived from the COCO (Common Objects in Context) 2017 dataset, which is a large-scale object detection, segmentation, and captioning dataset. The COCO 2017 dataset has been adapted here specifically for the task of image colorization.
+
+## Dataset Description
+
+- **Original Dataset:** [COCO 2017](https://cocodataset.org/)
+- **Task:** Image Colorization
+- **License:** [COCO dataset license](https://cocodataset.org/#termsofuse)
+
+## Data origin
+
+* Data originates from [cocodataset.org](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)
+
+## Format
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
+        num_rows: 112268
+    })
+    validation: Dataset({
+        features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
+        num_rows: 5000
+    })
+})
+```
+
+## Usage
+
+### Download image data and unzip
+  
+```bash
+cd PATH_TO_IMAGE_FOLDER
+
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+
+unzip train2017.zip
+unzip val2017.zip
+```
+
+### Loading the Dataset
+
+You can load this dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the train split of the colorization dataset
+train_dataset = load_dataset("nickpai/coco2017-colorization", split="train")
+
+# Load the validation split of the colorization dataset
+val_dataset = load_dataset("nickpai/coco2017-colorization", split="validation")
+```
+
+## Filtering Criteria
+
+### 1. Grayscale Images
+- Images in grayscale mode are identified and removed.
+- Grayscale images lack color information and may not be suitable for certain tasks.
+
+### 2. Identical Color Histograms
+- Images with identical histograms across color channels (red, green, and blue) are removed.
+- Such images may lack sufficient color variation, affecting model training and performance.
+
+### 3. Low Color Variance
+- Images with low color variance, determined by a specified threshold, are removed.
+- Low color variance can indicate poor image quality or uniform color distribution.