KaraAgroAI
commited on
Commit
•
cd63a9d
1
Parent(s):
9532f26
Update README.md
Browse files
README.md
CHANGED
@@ -70,9 +70,21 @@ yolov5 train --data data.yaml --img 640 --batch 16 --weights KaraAgroAI/CADI-AI
|
|
70 |
|
71 |
### Model performance
|
72 |
|
73 |
-
| Precision | Recall |
|
74 |
-
| --- | --- | --- | --- |
|
75 |
-
| 0.663 | 0.632 | 0.648 | 0.291 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
76 |
|
77 |
### Example prediction
|
78 |
|
|
|
70 |
|
71 |
### Model performance
|
72 |
|
73 |
+
| Class | Precision | Recall | mAP@50 | mAP@50-95 |
|
74 |
+
| --- | --- | --- | --- | --- |
|
75 |
+
| all | 0.663 | 0.632 | 0.648 | 0.291 |
|
76 |
+
| insect | 0.794 | 0.811 | 0.815 | 0.39 |
|
77 |
+
| abiotic | 0.682 | 0.514 | 0.542 | 0.237 |
|
78 |
+
| disease | 0.594 | 0.571 | 0.588 | 0.248 |
|
79 |
+
|
80 |
+
### Limitations of the Model
|
81 |
+
The model has a few limitations that affect its performance in distinguishing between the disease class and the abiotic class.
|
82 |
+
The primary challenge lies in the similarity between these two classes within a typical farm setting.
|
83 |
+
The model may encounter difficulties in accurately differentiating between them due to their overlapping characteristics.
|
84 |
+
This limitation is an inherent challenge in the dataset and can impact the model's accuracy when classifying these classes.
|
85 |
+
|
86 |
+
However, it is worth noting that the model exhibits strong performance when it comes to the insect class.
|
87 |
+
This is attributed to the distinct characteristics of insect class, which make them easier to identify and classify accurately.
|
88 |
|
89 |
### Example prediction
|
90 |
|