43 lines
1.8 KiB
Markdown
43 lines
1.8 KiB
Markdown
# Inference with existing models
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- [Inference with existing models](#inference-with-existing-models)
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- [Inference on a given image](#inference-on-a-given-image)
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MMPretrain provides pre-trained models in [Model Zoo](../modelzoo_statistics.md).
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This note will show **how to use existing models to inference on given images**.
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As for how to test existing models on standard datasets, please see this [guide](./train_test.md#test)
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## Inference on a given image
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MMPretrain provides high-level Python APIs for inference on a given image:
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- [`get_model`](mmpretrain.apis.get_model): Get a model with the model name.
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- [`init_model`](mmpretrain.apis.init_model): Initialize a model with a config and checkpoint
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- [`inference_model`](mmpretrain.apis.inference_model): Inference on a given image
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Here is an example of building the model and inference on a given image by using ImageNet-1k pre-trained checkpoint.
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```{note}
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You can use `wget https://github.com/open-mmlab/mmclassification/raw/master/demo/demo.JPEG` to download the example image or use your own image.
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```
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```python
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from mmpretrain import get_model, inference_model
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img_path = 'demo.JPEG' # you can specify your own picture path
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# build the model from a config file and a checkpoint file
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model = get_model('resnet50_8xb32_in1k', pretrained=True, device="cpu") # device can be 'cuda:0'
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# test a single image
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result = inference_model(model, img_path)
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```
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`result` is a dictionary containing `pred_label`, `pred_score`, `pred_scores` and `pred_class`, the result is as follows:
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```text
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{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}
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```
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An image demo can be found in [demo/image_demo.py](https://github.com/open-mmlab/mmclassification/blob/pretrain/demo/image_demo.py).
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