Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- vision transformer
+- agriculture
+- plant disease detection
+- smart farming
+- image classification
+license: mit
+metrics:
+- accuracy
+base_model:
+- WinKawaks/vit-tiny-patch16-224
+pipeline_tag: image-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Smart Farming Disease Detection Transformer
 
+This model is a Vision Transformer (ViT) designed to identify plant diseases in crops as part of a smart agricultural farming system. It has been trained on a diverse dataset of plant images, including different disease categories affecting crops such as corn, potato, rice, and wheat. The model aims to provide farmers and agronomists with real-time disease detection for better crop management.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This Vision Transformer model has been fine-tuned to classify various plant diseases commonly found in agricultural settings. The model can classify diseases in crops such as corn, potato, rice, and wheat, identifying diseases like rust, blight, leaf spots, and others. The goal is to enable precision farming by helping farmers detect diseases early and take appropriate actions.
+
+- **Developed by:** Wambugu Kinyua
+- **Model type:** Vision Transformer (ViT)
+- **Languages (NLP):** N/A (Computer Vision Model)
+- **License:** Apache 2.0
+- **Finetuned from model:** (WinKawaks/vit-tiny-patch16-224)[https://huggingface.co/WinKawaks/vit-tiny-patch16-224]
+- **Input:** Images of crops (RGB format)
+- **Output:** Disease classification labels (healthy or diseased categories)
+## Diseases  from the  model 
+
+| Crop   | Diseases Identified          |
+|--------|------------------------------|
+| Corn   | Common Rust                  |
+| Corn   | Gray Leaf Spot               |
+| Corn   | Healthy                      |
+| Corn   | Leaf Blight                  |
+| -      | Invalid                      |
+| Potato | Early Blight                 |
+| Potato | Healthy                      |
+| Potato | Late Blight                  |
+| Rice   | Brown Spot                   |
+| Rice   | Healthy                      |
+| Rice   | Hispa                        |
+| Rice   | Leaf Blast                   |
+| Wheat  | Brown Rust                   |
+| Wheat  | Healthy                      |
+| Wheat  | Yellow Rust                  |
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used directly to classify images of crops to detect plant diseases. It is especially useful for precision farming, enabling users to monitor crop health and take early interventions based on the detected disease.
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+This model can be fine-tuned on other agricultural datasets for specific crops or regions to improve its performance or be integrated into larger precision farming systems that include other features like weather predictions and irrigation control.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not designed for non-agricultural image classification tasks or for environments with insufficient or very noisy data. Misuse includes using the model in areas with vastly different agricultural conditions from those it was trained on.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may exhibit bias toward the crops and diseases present in the training dataset, leading to lower performance on unrepresented diseases or crop varieties.
+- False negatives (failing to detect a disease) may result in untreated crop damage, while false positives could lead to unnecessary interventions.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should evaluate the model on their specific crops and farming conditions. Regular updates and retraining with local data are recommended for optimal performance.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+import matplotlib.pyplot as plt
+from PIL import Image, UnidentifiedImageError
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+label2id= {'Corn___Common_Rust': '0',
+  'Corn___Gray_Leaf_Spot': '1',
+  'Corn___Healthy': '2',
+  'Corn___Leaf_Blight': '3',
+  'Invalid': '4',
+  'Potato___Early_Blight': '5',
+  'Potato___Healthy': '6',
+  'Potato___Late_Blight': '7',
+  'Rice___Brown_Spot': '8',
+  'Rice___Healthy': '9',
+  'Rice___Hispa': '10',
+  'Rice___Leaf_Blast': '11',
+  'Wheat___Brown_Rust': '12',
+  'Wheat___Healthy': '13',
+  'Wheat___Yellow_Rust': '14'},
+id2label  = {'0': 'Corn___Common_Rust',
+  '1': 'Corn___Gray_Leaf_Spot',
+  '2': 'Corn___Healthy',
+  '3': 'Corn___Leaf_Blight',
+  '4': 'Invalid',
+  '5': 'Potato___Early_Blight',
+  '6': 'Potato___Healthy',
+  '7': 'Potato___Late_Blight',
+  '8': 'Rice___Brown_Spot',
+  '9': 'Rice___Healthy',
+  '10': 'Rice___Hispa',
+  '11': 'Rice___Leaf_Blast',
+  '12': 'Wheat___Brown_Rust',
+  '13': 'Wheat___Healthy',
+  '14': 'Wheat___Yellow_Rust'}
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('WinKawaks/vit-tiny-patch16-224')
+model = ViTForImageClassification.from_pretrained(
+    'wambugu1738/crop_leaf_diseases_vit',
+    num_labels=15,
+    label2id=label2id,
+    id2label=id2label,
+    ignore_mismatched_sizes=True
+)
+
+from PIL import Image
+image = Image.open('path_to_image')
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[str(predicted_class_idx)])
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
+The model was trained on a dataset containing images of various crops with labeled diseases, including the following categories:
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- **Corn**: Common Rust, Gray Leaf Spot, Leaf Blight, Healthy
+- **Potato**: Early Blight, Late Blight, Healthy
+- **Rice**: Brown Spot, Hispa, Leaf Blast, Healthy
+- **Wheat**: Brown Rust, Yellow Rust, Healthy
 
-#### Preprocessing [optional]
+The dataset also includes images captured under various lighting conditions and angles to simulate real-world farming scenarios.
 
-[More Information Needed]
+### Training Procedure
 
+The model was fine-tuned using a vision transformer architecture pre-trained on the ImageNet dataset. The dataset was preprocessed by resizing the images and normalizing the pixel values.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+- **Batch size:** 32
+- **Learning rate:** 2e-5
+- **Epochs:** 4
+- **Optimizer:** AdamW
+- **Precision:** fp16
 
-#### Testing Data
+### Evaluation
+![Confusion matrix](conf_mat.png)
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+#### Testing Data, Factors & Metrics
 
-#### Factors
+The model was evaluated using a validation set consisting of 20% of the original dataset, with the following metrics:
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- **Accuracy:** 98%
+- **Precision:** 97%
+- **Recall:** 97%
+- **F1 Score:** 96%
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Carbon emissions during model training can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** NVIDIA L40S
+- **Hours used:** 1 hours
+- **Cloud Provider:** Lightning AI
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses a Vision Transformer architecture to learn image representations and classify them into disease categories. Its self-attention mechanism enables it to capture global contextual information in the images, making it suitable for agricultural disease detection.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- NVIDIA L40S GPUs
+- 48 GB RAM
+- SSD storage for fast I/O
 
 #### Software
 
-[More Information Needed]
+- Python 3.9
+- PyTorch 2.4.1+cu121
+- Transformers library by Hugging Face
 
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model in your research or applications, please cite it as:
 
 **BibTeX:**
 
-[More Information Needed]
+```
+@misc{kinyua2024smartfarming,
+  title={Smart Farming Disease Detection Transformer},
+  author={Wambugu Kinyua},
+  year={2024},
+  publisher={Hugging Face},
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+Kinyua, W. (2024). Smart Farming Disease Detection Transformer. Hugging Face.
 
 ## Model Card Contact
 
-[More Information Needed]
+For further inquiries, contact: [email protected]
+```