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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- uoft-cs/cifar10 |
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language: |
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- en |
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base_model: |
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- facebook/metaclip-2-worldwide-s16 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- cifar10 |
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--- |
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%3C!-- HTML_TAG_END --> |
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# **MetaCLIP-2-Cifar10** |
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> **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. |
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> It is designed to identify and categorize images into the ten CIFAR-10 object classes using the **MetaClip2ForImageClassification** architecture. |
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>[!note] |
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MetaCLIP 2: A Worldwide Scaling Recipe : https://huggingface.co/papers/2507.22062 |
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``` |
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Classification report: |
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precision recall f1-score support |
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airplane 0.9813 0.9685 0.9748 2000 |
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automobile 0.9777 0.9850 0.9813 2000 |
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bird 0.9560 0.9560 0.9560 2000 |
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cat 0.9104 0.9395 0.9247 2000 |
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deer 0.9566 0.9580 0.9573 2000 |
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dog 0.9476 0.9215 0.9343 2000 |
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frog 0.9774 0.9735 0.9755 2000 |
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horse 0.9704 0.9670 0.9687 2000 |
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ship 0.9782 0.9890 0.9836 2000 |
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truck 0.9774 0.9735 0.9755 2000 |
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accuracy 0.9631 20000 |
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macro avg 0.9633 0.9632 0.9632 20000 |
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weighted avg 0.9633 0.9631 0.9632 20000 |
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``` |
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%3C!-- HTML_TAG_END --> |
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--- |
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The model classifies images into the following categories: |
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* **Class 0:** airplane |
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* **Class 1:** automobile |
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* **Class 2:** bird |
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* **Class 3:** cat |
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* **Class 4:** deer |
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* **Class 5:** dog |
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* **Class 6:** frog |
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* **Class 7:** horse |
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* **Class 8:** ship |
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* **Class 9:** truck |
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# **Run with Transformers** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import AutoModelForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/MetaCLIP-2-Cifar10" |
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model = AutoModelForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def cifar10_classification(image): |
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"""Predicts the CIFAR-10 class represented in an image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "airplane", |
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"1": "automobile", |
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"2": "bird", |
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"3": "cat", |
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"4": "deer", |
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"5": "dog", |
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"6": "frog", |
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"7": "horse", |
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"8": "ship", |
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"9": "truck" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=cifar10_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="CIFAR-10 Classification", |
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description="Upload an image to classify it into one of the CIFAR-10 categories." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Sample Inference:** |
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# **Intended Use:** |
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The **MetaCLIP-2-Cifar10** model is designed for object classification across the ten CIFAR-10 categories. |
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Potential use cases include: |
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* **Educational & Research Applications:** Benchmarking experiments, model comparison, and deep learning studies. |
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* **Lightweight Vision Systems:** Useful for systems requiring simple object recognition. |
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* **Dataset Exploration:** Assisting in data inspection, annotation, and visualization. |
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* **Prototype Systems:** Ideal for rapid prototyping in classification pipelines. |
