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--- |
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language: |
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- en |
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library_name: ultralytics |
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pipeline_tag: image-classification |
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tags: |
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- fer2013 |
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--- |
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## How to Use |
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To use this model in your project, follow the steps below: |
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### 1. Installation |
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Ensure you have the `ultralytics` library installed, which is used for YOLO models: |
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```bash |
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pip install ultralytics |
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``` |
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```text |
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# classs |
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angry |
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disgusted |
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fearful |
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happy |
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neutral |
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sad |
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surprised |
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``` |
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### 2. Load the Model |
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You can load the model and perform detection on an image as follows: |
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```python |
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from ultralytics import YOLO |
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# Load the model |
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model = YOLO("./feryolo-11x-64.pt") |
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# Perform detection on an image |
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results = model("image.png") |
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# Display or process the results |
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results.show() # This will display the image with detected objects |
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``` |
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### 3. Model Inference |
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The results object contains bounding boxes, labels (e.g., numbers or operators), and confidence scores for each detected object. |
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Access them like this: |
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```python |
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# View results |
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for r in results: |
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print(r.probs) # print the Probs object containing the detected class probabilities |
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``` |
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