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"final text_encoder_type: bert-base-uncased\n"
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"Downloading model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]"
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"/root/miniconda3/lib/python3.8/site-packages/transformers/modeling_utils.py:881: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
" warnings.warn(\n",
"/root/miniconda3/lib/python3.8/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
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"source": [
"from groundingdino.util.inference import load_model, load_image, predict, annotate\n",
"import cv2\n",
"\n",
"model = load_model(\"groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"../04-06-segment-anything/weights/groundingdino_swint_ogc.pth\")\n",
"IMAGE_PATH = \".asset/cat_dog.jpeg\"\n",
"TEXT_PROMPT = \"chair . person . dog .\"\n",
"BOX_TRESHOLD = 0.35\n",
"TEXT_TRESHOLD = 0.25\n",
"\n",
"image_source, image = load_image(IMAGE_PATH)\n",
"\n",
"boxes, logits, phrases = predict(\n",
" model=model,\n",
" image=image,\n",
" caption=TEXT_PROMPT,\n",
" box_threshold=BOX_TRESHOLD,\n",
" text_threshold=TEXT_TRESHOLD\n",
")\n",
"\n",
"annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\n",
"cv2.imwrite(\"annotated_image.jpg\", annotated_frame)"
]
}
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