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README.md

@@ -1,29 +1,44 @@
----
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype:
-      image:
-        mode: L
-  - name: image_id
-    dtype: string
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: num_annotations
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 1933763.0
-    num_examples: 129
-  download_size: 1948584
-  dataset_size: 1933763.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+
+    ---
+    license: apache-2.0
+    tags:
+    - computer-vision
+    - image-segmentation
+    - leaf-dataset
+    ---
+
+    # In_the_Lab_masks
+
+    This is a dataset for leaf segmentation, created by **Subh775**.
+
+    ## Dataset Description
+
+    This dataset contains in-the-lab single images of plant leaves (Tomato) and their corresponding binary segmentation masks. It was annotated using CVAT Segment Anything 2.0 and is intended for training computer vision models for segmentation tasks.
+
+    The dataset includes additional metadata extracted from the annotation process.
+
+    ### Features
+
+    - `image`: The original RGB image of the plant leaf/leaves.
+    - `mask`: The binary, single-channel (grayscale) segmentation mask.
+    - `image_id`: The original filename of the image.
+    - `width`: The original width of the image.
+    - `height`: The original height of the image.
+    - `num_annotations`: The number of individual leaves annotated in the image.
+
+    ## Dataset Structure
+
+    The dataset consists of 129 image-mask pairs.
+
+    **Example Usage:**
+
+    ```python
+    from datasets import load_dataset
+    dataset = load_dataset("Subh775/In_the_Lab_masks")
+    # Access a sample
+    sample = dataset['train'][0]
+    image = sample['image']
+    mask = sample['mask']
+    print(f"Image size: {image.size}")
+    print(f"Number of annotations in this image: {sample['num_annotations']}")
+