Update README.md

README.md

@@ -2,8 +2,24 @@
 license: cc-by-nc-4.0
 datasets:
 - uoft-cs/cifar10
+language:
+- en
+base_model:
+- facebook/metaclip-2-worldwide-s16
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- text-generation-inference
+- cifar10
 ---
 
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mZz2vZy1IENHbtmXm1lUe.png)
+
+# **MetaCLIP-2-Cifar10**
+
+> **MetaCLIP-2-Cifar10** is an image classification vision–language encoder model fine-tuned from **facebook/metaclip-2-worldwide-s16** for a single-label classification task.
+> It is designed to identify and categorize images into the ten CIFAR-10 object classes using the **MetaClip2ForImageClassification** architecture.
+
 ```
 Classification report:
 
@@ -25,4 +41,88 @@ Classification report:
 weighted avg     0.9633    0.9631    0.9632     20000
 ```
 
-![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/dr7B2yAcfNEJ6ScY6XNC5.png)
+![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/dr7B2yAcfNEJ6ScY6XNC5.png)
+
+---
+
+The model classifies images into the following categories:
+
+* **Class 0:** airplane
+* **Class 1:** automobile
+* **Class 2:** bird
+* **Class 3:** cat
+* **Class 4:** deer
+* **Class 5:** dog
+* **Class 6:** frog
+* **Class 7:** horse
+* **Class 8:** ship
+* **Class 9:** truck
+
+# **Run with Transformers**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import AutoModelForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/MetaCLIP-2-Cifar10"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def cifar10_classification(image):
+    """Predicts the CIFAR-10 class represented in an image."""
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    labels = {
+        "0": "airplane",
+        "1": "automobile",
+        "2": "bird",
+        "3": "cat",
+        "4": "deer",
+        "5": "dog",
+        "6": "frog",
+        "7": "horse",
+        "8": "ship",
+        "9": "truck"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=cifar10_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="CIFAR-10 Classification",
+    description="Upload an image to classify it into one of the CIFAR-10 categories."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**
+
+The **MetaCLIP-2-Cifar10** model is designed for object classification across the ten CIFAR-10 categories.
+Potential use cases include:
+
+* **Educational & Research Applications:** Benchmarking experiments, model comparison, and deep learning studies.
+* **Lightweight Vision Systems:** Useful for systems requiring simple object recognition.
+* **Dataset Exploration:** Assisting in data inspection, annotation, and visualization.
+* **Prototype Systems:** Ideal for rapid prototyping in classification pipelines.