mmclassification/docs/en/user_guides/inference.md

Inference with existing models

MMClassification provides pre-trained models for classification in Model Zoo. This note will show how to use existing models to inference on given images.

As for how to test existing models on standard datasets, please see this guide

Inference on a given image

MMClassification provides high-level Python APIs for inference on a given image:

• get_model: Get a model with the model name.
• init_model: Initialize a model with a config and checkpoint
• inference_model: Inference on a given image

Here is an example of building the model and inference on a given image by using ImageNet-1k pre-trained checkpoint.

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.

from mmpretrain import get_model, inference_model

img_path = 'demo.JPEG'   # you can specify your own picture path

# build the model from a config file and a checkpoint file
model = get_model('resnet50_8xb32_in1k', pretrained=True, device="cpu")  # device can be 'cuda:0'
# test a single image
result = inference_model(model, img_path)

result is a dictionary containing pred_label, pred_score, pred_scores and pred_class, the result is as follows:

{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}

An image demo can be found in demo/image_demo.py.