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--- |
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license: apache-2.0 |
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tags: |
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- Bounding box detection |
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- PyTorch |
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--- |
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This repository contains code that you can use to train or load [Faster R-CNN](https://arxiv.org/pdf/1504.08083.pdf) models in half mode easily. |
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Below is an example of how to load pretrained weights in half mode. |
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``` |
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import numpy as np |
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from PIL import Image |
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import json |
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from frcnn.visualizing_image import SingleImageViz |
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from frcnn.processing_image import Preprocess |
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from frcnn.modeling_frcnn import GeneralizedRCNN |
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from frcnn.utils import Config, decode_image |
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max_detections = 36 |
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frcnn_config = json.load(open("frcnn/config.jsonl")) |
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frcnn_config = Config(frcnn_config) |
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image_preprocessor= Preprocess(frcnn_config).half().cuda() |
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box_segmentation_model= GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned", frcnn_config).half().cuda() |
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img_url = 'image.png' |
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raw_image = Image.open(img_url).convert('RGB') |
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frcnn_output = decode_image(np.asarray(raw_image), box_segmentation_model, image_preprocessor, max_detections=max_detections) |
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``` |
