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--- |
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language: |
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- en |
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thumbnail: "/assets/image.jpg" |
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tags: |
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- image-classification |
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- computer-vision |
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- agriculture |
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- maize-diseases |
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- agroeye |
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- eligapris |
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- grey |
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license: mit |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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--- |
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# agroEye |
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This model is designed to detect diseases in maize (corn) leaves using computer vision techniques. |
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## Model description |
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The agroEye is a convolutional neural network (CNN) trained to classify images of maize leaves into four categories: Healthy, Gray Leaf Spot, Blight, and Common Rust. It aims to assist farmers and agricultural professionals in quickly identifying common maize diseases, potentially leading to earlier interventions and improved crop management. |
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### Intended uses & limitations |
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The model is intended for use as a diagnostic tool to assist in the identification of maize leaf diseases. It should be used in conjunction with expert knowledge and not as a sole means of diagnosis. The model's performance may vary depending on image quality, lighting conditions, and the presence of diseases or conditions not included in the training dataset. |
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**Limitations:** |
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- The model is trained on a specific dataset and may not generalize well to significantly different growing conditions or maize varieties. |
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- It is not designed to detect diseases other than the four categories it was trained on. |
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- Performance on images with multiple diseases present has not been extensively tested. |
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- The model should not be used as a replacement for professional agricultural advice. |
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### How to use |
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Here's a basic example of how to use the model: |
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```python |
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import tensorflow as tf |
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from PIL import Image |
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import numpy as np |
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import json |
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import tensorflow as tf |
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from huggingface_hub import snapshot_download |
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# Download the entire model directory |
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model_dir = snapshot_download(repo_id="eligapris/agroeye", |
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local_dir="path/to/model") |
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# Load the model |
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model = tf.saved_model.load('path/to/model') |
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# Now you can use the model for inference |
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# Load and preprocess the image |
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img = Image.open('/path/to/image.jpg') |
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img = img.resize((300, 300 * img.size[1] // img.size[0])) |
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img_array = np.array(img)[None] |
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# Make prediction |
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inp = tensorflow.constant(img_array, dtype='float32') |
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prediction = model(inp)[0].numpy() |
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# Load class names |
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with open('path/to/model/classes.json', 'r') as f: |
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class_names = json.load(f) |
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# Get the predicted class |
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predicted_class = list(class_names.keys())[prediction.argmax()] |
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print(f"Predicted class: {predicted_class}") |
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``` |
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Here's a detailed output of model prediction: |
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```python |
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import tensorflow as tf |
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from PIL import Image |
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import numpy as np |
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import json |
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import tensorflow as tf |
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from huggingface_hub import snapshot_download |
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# Download the entire model directory |
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model_dir = snapshot_download(repo_id="eligapris/agroeye", |
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local_dir="path/to/model") |
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# Load the model |
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model = tf.saved_model.load('path/to/model') |
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# Now you can use the model for inference |
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# Load and preprocess the image |
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img = Image.open('/path/to/image.jpg') |
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img = img.resize((300, 300 * img.size[1] // img.size[0])) |
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img_array = np.array(img)[None] |
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# Make prediction |
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inp = tensorflow.constant(img_array, dtype='float32') |
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prediction = model(inp)[0].numpy() |
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# Load class names and details |
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with open('model/classes_detailed.json', 'r') as f: |
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data = json.load(f) |
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class_names = data['classes'] |
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class_details = data['details'] |
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# Get the predicted class |
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predicted_class = list(class_names.keys())[prediction.argmax()] |
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predicted_class_label = class_names[predicted_class] |
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print(f"Predicted class: {predicted_class} (Label: {predicted_class_label})") |
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# Print detailed information about the predicted class |
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if predicted_class in class_details: |
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details = class_details[predicted_class] |
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print("\nDetailed Information:") |
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for key, value in details.items(): |
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if isinstance(value, list): |
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print(f"{key.capitalize()}:") |
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for item in value: |
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print(f" - {item}") |
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else: |
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print(f"{key.capitalize()}: {value}") |
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# Print general notes |
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print("\nGeneral Notes:") |
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for note in data['general_notes']: |
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print(f"- {note}") |
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``` |
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### Test the colab |
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``` |
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https://colab.research.google.com/drive/13-S-obR6MZDDP5kgj6ytsbFiNKzzfXbp |
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``` |
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## Ethical considerations |
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- The model's predictions should not be used as the sole basis for agricultural decisions that may impact food security or farmers' livelihoods. |
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- There may be biases in the training data that could lead to reduced performance for certain maize varieties or growing conditions not well-represented in the dataset. |
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- Users should be made aware of the model's limitations and the importance of expert validation. |
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## Model Card Authors |
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Grey |
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## Model Card Contact |
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eligapris |
