Init README

README.md

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+---
+language:
+- en
+pretty_name: COCO2017
+size_categories:
+- 100K<n<1M
+task_categories:
+- image-to-text
+task_ids:
+- image-captioning
+tags:
+- coco
+- image-captioning
+---
+# coco2017
+
+Image-text pairs from [COCO2017](https://cocodataset.org/#download).
+
+## Data origin
+
+* Data originates from [cocodataset.org](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)
+* While `coco-karpathy` uses a dense format (with several sentences and sendids per row), `coco-karpathy-long` uses a long format with one `sentence` (aka caption) and `sendid` per row. `coco-karpathy-long` uses the first five sentences and therefore is five times as long as `coco-karpathy`.
+    * `phiyodr/coco2017`: One row corresponds one image with several sentences.
+    * `phiyodr/coco2017-long`: One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details.
+
+## Format
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
+        num_rows: 118287
+    })
+    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_FOLDER
+
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+#wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip # zip not needed: everything you need is in load_dataset("phiyodr/coco2017")
+
+unzip train2017.zip
+unzip val2017.zip
+```
+
+* Load dataset in Python
+  
+```python
+import os
+from datasets import load_dataset
+PATH_TO_IMAGE_FOLDER = "COCO2017"
+
+def create_full_path(example):
+    """Create full path to image using `base_path` to COCO2017 folder."""
+    example["image_path"] = os.path.join(PATH_TO_IMAGE_FOLDER, example["filepath"], example["filename"])
+    return example
+
+dataset = load_dataset("phiyodr/coco2017")
+dataset = dataset.map(create_full_path)
+```