Update README.md

README.md

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----
-license: mit
----
+---
+task_categories:
+- image-segmentation
+- image-classification
+language:
+- en
+tags:
+- agritech
+- hyperspectral
+- spectroscopy
+- fruit
+- sub-class classification
+- detection
+size_categories:
+- 10K<n<100K
+license: mit
+---
+
+# Living Optics Orchard Dataset
+## Overview
+This dataset contains 435 images of captured in one of the UK's largest orchards, using the Living Optics Camera.
+
+The data consists of RGB images, sparse spectral samples and instance segmentation masks.
+
+The dataset is derived from 44 unique raw files corresponding to 435 frames. 
+Therefore, multiple frames could originate from the same raw file. 
+This structure emphasized the need for a split strategy that avoided data leakage. 
+To ensure robust evaluation, the dataset was divided using an 8:2 split, with splitting performed at the raw file level rather than the frame level. 
+This strategy guaranteed that all frames associated with a specific raw file were confined to either the training set or the test set, eliminating the 
+risk of overlapping information between the two sets.
+The dataset contains 3,785 instances of Royal Gala Apples, 2,523 instances of Pears, and 73 instances of Cox Apples, summing to a total of 6,381 labelled instances. 
+
+The spectra which do not lie within a labelled segmentation mask can be used for negative sampling when training classifiers.
+
+
+Additional unlabelled data is available upon request.
+
+
+## Classes
+The training dataset contains 3 classes:
+- 🍎 cox apple - 3,605 total spectral samples
+- 🍎 royal gala apple - 13,282 total spectral samples
+- 🍐 pear - 34,398 total spectral samples
+
+The remaining 1,855,755 spectra are unlabelled and can be considered a single "background " class.
+
+## Requirements
+
+- [lo-sdk](https://www.livingoptics.com/register-for-download-sdk/)
+- [lo-data](https://huggingface.co/spaces/LivingOptics/README/discussions/3)
+
+
+## Download instructions
+
+### Command line
+```commandline
+mkdir -p hyperspectral-orchard
+huggingface-cli download LivingOptics/hyperspectral-orchard --repo-type dataset --local-dir hyperspectral-orchard
+```
+
+### Python
+```python 
+from huggingface_hub import snapshot_download
+dataset_path = snapshot_download(repo_id="LivingOptics/hyperspectral-orchard", repo_type="dataset")
+print(dataset_path)
+```
+
+## Usage
+
+```python
+import os.path as op
+import numpy.typing as npt
+from typing import List, Dict, Generator
+from lo.data.tools import Annotation, LODataItem, LOJSONDataset, draw_annotations
+from lo.data.dataset_visualisation import get_object_spectra, plot_labelled_spectra
+from lo.sdk.api.acquisition.io.open import open as lo_open
+
+# Load the dataset
+path_to_download = op.expanduser("~/Downloads/hyperspectral-orchard")
+dataset = LOJSONDataset(path_to_download)
+
+# Get the training data as an iterator 
+training_data: List[LODataItem] = dataset.load("train")
+
+# Inspect the data
+lo_data_item: LODataItem
+for lo_data_item in training_data[:3]:
+
+    draw_annotations(lo_data_item)
+
+    ann: Annotation
+    for ann in lo_data_item.annotations:
+        print(ann.class_name, ann.category, ann.subcategories)
+
+# Plot the spectra for each class
+fig, ax = plt.subplots(1)
+object_spectra_dict = {}
+class_numbers_to_labels = {0: "background_class"}
+for lo_data_item in training_data:
+    object_spectra_dict, class_numbers_to_labels = get_object_spectra(
+        lo_data_item, object_spectra_dict, class_numbers_to_labels
+    )
+
+plot_labelled_spectra(object_spectra_dict, class_numbers_to_labels, ax)
+plt.show()    
+```
+
+See our [Spatial Spectral ML](https://github.com/livingoptics/spatial-spectral-ml) project for an example of how to train and run a segmentation and spectral classification algoirthm using this dataset.