Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 tags:
-- image-classificaiton
+- image-classification
 - vit
 - pytorch
 license: apache-2.0
@@ -12,224 +12,77 @@ metrics:
 - f1
 ---
 
-
 # Umsakwa/Uddayvit-image-classification-model
 
-This is a Vision Transformer (ViT)-based model fine-tuned for **image classification tasks**. It classifies images into predefined categories and is suitable for various real-world use cases, including object detection, plant disease identification, and more.
-
-
+This Vision Transformer (ViT) model has been fine-tuned for image classification tasks on the [Beans Dataset](https://huggingface.co/datasets/beans), which consists of images of beans categorized into three classes:
 
+- **Angular Leaf Spot**
+- **Bean Rust**
+- **Healthy**
 
 ## Model Details
 
-- **Model Architecture**: Vision Transformer (ViT)
+- **Architecture**: Vision Transformer (ViT)
+- **Base Model**: `google/vit-base-patch16-224-in21k`
 - **Framework**: PyTorch
-- **Training Data**: The model was trained on [Your Dataset Name]. Include details such as the dataset size, number of classes, and source (e.g., public dataset on Hugging Face or custom dataset).
-- **Dataset Link**: [Dataset on Hugging Face](https://huggingface.co/datasets/your-dataset-name)
-- **Input Data**: The model accepts RGB images in standard formats (e.g., JPEG, PNG) and preprocesses them to the required input size (e.g., 224x224).
-- **Preprocessing**: The model uses a processor that tokenizes and normalizes the input images.
-- 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+- **Task**: Image Classification
+- **Labels**: 3 (angular_leaf_spot, bean_rust, healthy)
+- **Input Shape**: 224x224 RGB images
+- **Training Dataset**: [Beans Dataset](https://huggingface.co/datasets/beans)
+- **Fine-Tuning**: The model was fine-tuned on the Beans dataset to classify plant diseases in beans.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Model Description
 
-- **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]
+The model uses the ViT architecture, which processes image patches using a transformer-based approach. It has been trained to classify bean diseases with high accuracy. This makes it particularly useful for agricultural applications, such as early disease detection and plant health monitoring.
 
-### Model Sources [optional]
+- **Developed by**: Udday (Umsakwa)
+- **Language(s)**: N/A (Image-based)
+- **License**: Apache-2.0
+- **Finetuned from**: `google/vit-base-patch16-224-in21k`
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository**: [Umsakwa/Uddayvit-image-classification-model](https://huggingface.co/Umsakwa/Uddayvit-image-classification-model)
 
 ## 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. -->
-
-[More Information Needed]
+This model can be directly used for classifying bean leaf images into one of three categories: angular leaf spot, bean rust, or healthy.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model may also be fine-tuned further for similar agricultural image classification tasks or integrated into larger plant health monitoring systems.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- The model is not suitable for non-agricultural image classification tasks without further fine-tuning.
+- Not robust to extreme distortions, occlusions, or very low-resolution images.
 
 ## Bias, Risks, and Limitations
 
-The model’s performance is tied to the quality of the training dataset. For datasets significantly different from the one used for training, fine-tuning might be required.
-It is not robust to extreme distortions, occlusions, or very low-resolution images.
-The model may have biases inherited from the dataset.
-
-[More Information Needed]
+- **Bias**: The dataset may contain biases due to specific environmental or geographic conditions of the sampled plants.
+- **Limitations**: Performance may degrade on datasets significantly different from the training dataset.
 
 ### 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 ensure the model is evaluated on their specific dataset before deployment.
+- Additional fine-tuning may be required for domain-specific applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-Frameworks Used:
-
-Transformers (Hugging Face)
-PyTorch
-Hyperparameters:
-
-Epochs: 5
-Batch Size: 16
-Learning Rate: 5e-5
-Optimizer: AdamW
-
-Loss Function: Cross-Entropy Loss
-
-Hardware Used:
-
-GPU: NVIDIA Tesla T4
-### 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
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- 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]
-
-## 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).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**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]
+To use this model for inference:
 
-## Model Card Authors [optional]
+```python
+from transformers import ViTForImageClassification, ViTImageProcessor
 
-[More Information Needed]
+# Load model and processor
+model = ViTForImageClassification.from_pretrained("Umsakwa/Uddayvit-image-classification-model")
+processor = ViTImageProcessor.from_pretrained("Umsakwa/Uddayvit-image-classification-model")
 
-## Model Card Contact
+# Prepare an image
+image = processor(images="path_to_image.jpg", return_tensors="pt")
 
-[More Information Needed]
+# Run inference
+outputs = model(**image)
+predictions = outputs.logits.argmax(-1)