upd md

.gitignore

@@ -0,0 +1,2 @@
+test.*
+*__pycache__*

README.md

@@ -22,8 +22,6 @@ dataset_info:
         names:
           '0': fresh
           '1': rotten
-  - name: quality
-    dtype: image
   - name: category
     dtype:
       class_label:
@@ -35,8 +33,6 @@ dataset_info:
           '4': oranges
           '5': potato
           '6': tomato
-  - name: category
-    dtype: image
   splits:
   - name: train
     num_bytes: 3125926
@@ -60,4 +56,28 @@ configs:
     path: default/test/data-*.arrow
 ---
 
-# Intro
+
+# Intro
+The Fruit and Vegetable Quality Dataset is a multi‑category image dataset designed for quality classification and produce recognition tasks. It contains over 19,000 images across seven fruit and vegetable types (apples, bananas, cucumbers, okra, oranges, potatoes, and tomatoes), each annotated with a binary quality label (fresh or rotten). The dataset is split into training (13,355 samples), validation (2,857), and test (2,867) sets, providing a standardized benchmark for developing and evaluating computer vision models in agricultural quality inspection. With an MIT license and a size range of 10K to 100K samples, the dataset supports academic and industrial research in tasks such as defect detection, quality grading, and species identification.
+
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset(
+    "RobotIX-Lab/fruit_quality",
+    name="default",
+    split="train",
+    cache_dir="./__pycache__",
+)
+for i in ds:
+    print(i)
+```
+
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/RobotIX-Lab/fruit_quality
+cd vtuber_emojis
+```
+
+## Mirror
+<https://www.modelscope.cn/datasets/RobotIX-Lab/fruit_quality>