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--- |
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tags: |
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- image-classification |
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- auto-train |
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- vision |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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datasets: |
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- Falah/Blood_8_classes_Dataset |
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license: apache-2.0 |
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pipeline_tag: image-classification |
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--- |
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# ResNet-50 v1.5 |
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ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. |
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Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. |
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This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch). |
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## Validation Metrics |
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No validation metrics available |
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### How to use |
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Here is how to use this model to classify an image of : |
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```python |
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from transformers import AutoImageProcessor, ResNetForImageClassification |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") |
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model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_label = logits.argmax(-1).item() |
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print(model.config.id2label[predicted_label]) |
