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--- |
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license: cc-by-sa-4.0 |
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datasets: |
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- KaraAgroAI/CADI-AI |
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language: |
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- en |
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metrics: |
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- mape |
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pipeline_tag: object-detection |
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tags: |
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- object detection |
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- vision |
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--- |
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## Cashew Disease Identification with AI (CADI-AI) Model |
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### Model Description |
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Object detection model trained using YOLO v5x. The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels. |
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CADI-AI dataset is available in hugging face dataset hub |
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## Intended uses & limitations |
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You can use the raw model for object detection on cashew images. |
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### How to use |
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- Load model and perform prediction: |
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```python |
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import torch |
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# load model |
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model = torch.hub.load('ultralytics/yolov5', 'KaraAgroAI/CADI-AI') |
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# Images |
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img = ['/path/to/CADI-AI-image.jpg']# batch of images |
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# set model parameters |
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model.conf = 0.20 # NMS confidence threshold |
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# perform inference |
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results = model(img, size=640) |
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# Results |
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results.print() |
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results.xyxy[0] # img1 predictions (tensor) |
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results.pandas().xyxy[0] # img1 predictions (pandas) |
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# parse results |
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predictions = results.pred[0] |
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boxes = predictions[:, :4] # x1, y1, x2, y2 |
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scores = predictions[:, 4] |
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categories = predictions[:, 5] |
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# show detection bounding boxes on image |
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results.show() |
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# save results into "results/" folder |
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results.save(save_dir='results/') |
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``` |
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- Finetune the model on your custom dataset: |
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```bash |
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yolov5 train --data data.yaml --img 640 --batch 16 --weights KaraAgroAI/CADI-AI --epochs 10 |
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``` |
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