Update README.md

README.md

@@ -21,7 +21,8 @@ metrics:
 
 Object detection model trained using [YOLO v5x](https://github.com/ultralytics/yolov5/releases), a SOTA object detection algorithm.  
 The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels. 
-CADI-AI dataset is available in hugging face dataset hub.
+The CADI-AI dataset is available via [Kaggle](https://www.kaggle.com/datasets/karaagroaiprojects/cadi-ai) and 
+[HuggingFace](https://huggingface.co/datasets/KaraAgroAI/CADI-AI).
 
 ## Intended uses & limitations
 
@@ -31,7 +32,7 @@ The model was initially developed to inform users whether cashew trees suffer fr
 - pest infection, i.e. damage to crops by insects or pests
 - disease, i.e. attacks on crops by microorganisms
 - abiotic stress caused by non-living factors (e.g. environmental factors like weather or soil conditions or the lack of mineral nutrients to the crop).
-  <br />
+
 KaraAgro AI developed the model for the initiatives 
 [Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)](https://www.giz.de/en/worldwide/108524.html) and 
 [FAIR Forward - Artificial Intelligence for All](https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/). 
@@ -106,7 +107,7 @@ However, it is worth noting that the model exhibits strong performance when it c
 This is attributed to the distinct characteristics of insect class, which make them easier to identify and classify accurately.
 
 ### Spaces 
-[CADI-AI Space](https://huggingface.co/spaces/KaraAgroAI/CADI-AI)
+[CADI-AI Spaces demonstration](https://huggingface.co/spaces/KaraAgroAI/CADI-AI)
 
 ### Example prediction