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Runtime error
| try: | |
| import detectron2 | |
| except: | |
| import os | |
| os.system('pip install git+https://github.com/facebookresearch/detectron2.git') | |
| from inference import * | |
| import gradio as gr | |
| import glob | |
| def gradio_app(image_path): | |
| """Helper function to run inference on provided image""" | |
| predictions, out_pil = run_inference(image_path) | |
| return out_pil | |
| # ----------------------------------------------------------------------------- | |
| # GRADIO APP | |
| # ----------------------------------------------------------------------------- | |
| title = "MBARI Monterey Bay Benthic Supercategory" | |
| description = "Gradio demo for MBARI Monterey Bay Benthic Supercategory: This " \ | |
| "is a RetinaNet model fine-tuned from the Detectron2 object " \ | |
| "detection platform's ResNet backbone to identify 20 benthic " \ | |
| "supercategories drawn from MBARI's remotely operated vehicle " \ | |
| "image data collected in Monterey Bay off the coast of Central " \ | |
| "California. The data is drawn from FathomNet and consists of " \ | |
| "32779 images that contain a total of 80683 localizations. The " \ | |
| "model was trained on an 85/15 train/validation split at the " \ | |
| "image level. DOI: 10.5281/zenodo.5571043. " | |
| examples = glob.glob("images/*.png") | |
| gr.Interface(gradio_app, | |
| inputs=[gr.inputs.Image(type="filepath")], | |
| outputs=gr.outputs.Image(type="pil"), | |
| enable_queue=True, | |
| title=title, | |
| description=description, | |
| examples=examples).launch() | |