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--- |
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datasets: |
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- imagenet-1k |
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library_name: transformers |
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pipeline_tag: image-classification |
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--- |
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# SwiftFormer |
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## Model description |
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The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. |
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SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2. |
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## Intended uses & limitations |
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## How to use |
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import requests |
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from PIL import Image |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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from transformers import ViTImageProcessor |
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processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-xs') |
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inputs = processor(images=image, return_tensors="pt") |
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from transformers.models.swiftformer import SwiftFormerForImageClassification |
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new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-xs') |
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output = new_model(inputs['pixel_values'], output_hidden_states=True) |
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logits = output.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", new_model.config.id2label[predicted_class_idx]) |
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## Limitations and bias |
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## Training data |
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The classification model is trained on the ImageNet-1K dataset. |
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## Training procedure |
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## Evaluation results |
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