{"id":53770,"date":"2025-02-16T09:11:29","date_gmt":"2025-02-16T01:11:29","guid":{"rendered":"https:\/\/fwq.ai\/blog\/53770\/"},"modified":"2025-02-16T09:11:29","modified_gmt":"2025-02-16T01:11:29","slug":"%e5%9b%be%e5%83%8f%e6%95%b0%e6%8d%ae%e9%9b%86%e8%87%aa%e5%8a%a8%e6%a0%87%e6%b3%a8%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/fwq.ai\/blog\/53770\/","title":{"rendered":"\u56fe\u50cf\u6570\u636e\u96c6\u81ea\u52a8\u6807\u6ce8\u6307\u5357"},"content":{"rendered":"<p>\u5728\u8fd9\u4e2a\u6fc0\u52a8\u4eba\u5fc3\u7684\u5192\u9669\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u7814\u7a76\u7528\u4e8e\u7269\u4f53\u68c0\u6d4b\u548c\u56fe\u50cf\u5206\u5272\u7684\u5c0f\u578b\u4f46\u5f3a\u5927\u7684\u6a21\u578b\u7684\u4e16\u754c\u3002\u6211\u4eec\u7684\u76ee\u6807\u662f\u4ec0\u4e48\uff1f\u5229\u7528\u5927\u578b\u6a21\u578b\u7684\u529b\u91cf\u6765\u521b\u5efa\u9ad8\u6548\u3001\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u96c6\uff0c\u8fd9\u4e9b\u6570\u636e\u96c6\u53ef\u4ee5\u8bad\u7ec3\u66f4\u5feb\u3001\u66f4\u5c0f\u7684\u6a21\u578b\uff0c\u800c\u4e0d\u4f1a\u5f71\u54cd\u6027\u80fd\u3002\u8ba9\u6211\u4eec\u5f00\u59cb\u5427\uff01<\/p>\n<p>\u672c\u6587\u9002\u7528\u4e8e\u90a3\u4e9b\u51c6\u5907\u4f7f\u7528 Grounding DINO\u3001SAM \u548c AutoDistill \u7b49\u6700\u5148\u8fdb\u7684\u6a21\u578b\/\u5de5\u5177\u6784\u5efa\u81ea\u5df1\u7684\u6570\u636e\u96c6\u7684\u4eba\u3002\u5982\u679c\u4f60\u66fe\u7ecf\u56e0\u6a21\u578b\u901f\u5ea6\u6162\u6216\u624b\u52a8\u6807\u6ce8\u7684\u9ebb\u70e6\u800c\u611f\u5230\u6cae\u4e27\uff0c\u8bf7\u4e0d\u8981\u5bb3\u6015\uff01\u6211\u4eec\u5c06\u81ea\u52a8\u751f\u6210\u6807\u6ce8\u6570\u636e\u7684\u8fc7\u7a0b\uff0c\u5e76\u4f7f\u7528 Roboflow \u5bf9\u5176\u8fdb\u884c\u6539\u8fdb\u4ee5\u786e\u4fdd\u8d28\u91cf\u3002<\/p>\n<h2>1\u3001\u6570\u636e\u96c6\u521b\u5efa\u7b80\u4ecb<\/h2>\n<p>\u521b\u5efa\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u96c6\u662f\u4efb\u4f55\u6210\u529f\u7684\u673a\u5668\u5b66\u4e60\u9879\u76ee\u7684\u57fa\u7840\u3002\u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u5982\u4f55\u5229\u7528 Grounding DINO \u548c SAM \u7b49\u5927\u578b\u6a21\u578b\u81ea\u52a8\u6807\u8bb0\u56fe\u50cf\u3002\u6211\u4eec\u8fd8\u5c06\u4f7f\u7528 Roboflow \u7b49\u5de5\u5177\u6765\u4f18\u5316\u8fd9\u4e9b\u6807\u7b7e\uff0c\u4ece\u800c\u5b9e\u73b0\u6d41\u7545\u9ad8\u6548\u7684\u5de5\u4f5c\u6d41\u7a0b\u3002<\/p>\n<p>\u5728\u8fd9\u6b21\u5192\u9669\u4e2d\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u5173\u6ce8\u4ee5\u4e0b\u57fa\u672c\u6b65\u9aa4\uff1a<\/p>\n<ul>\n<li>Grounding DINO \u7528\u4e8e\u57fa\u4e8e\u6587\u672c\u63d0\u793a\u7684\u81ea\u52a8\u68c0\u6d4b<\/li>\n<li>SAM\u6a21\u578b\u7528\u4e8e\u7cbe\u786e\u7684\u56fe\u50cf\u5206\u5272<\/li>\n<li>AutoDistill \u7528\u4e8e\u7b80\u5316\u6570\u636e\u96c6\u521b\u5efa<\/li>\n<li>Roboflow \u7528\u4e8e\u6807\u7b7e\u6539\u8fdb\u548c\u589e\u5f3a<\/li>\n<\/ul>\n<blockquote><p>\n  Grounding\n<\/p><\/blockquote>\n<p>Grounding\u5c06 AI \u77e5\u8bc6\u4e0e\u73b0\u5b9e\u4e16\u754c\u7684\u793a\u4f8b\u8054\u7cfb\u8d77\u6765\uff0c\u63d0\u9ad8\u51c6\u786e\u6027\u5e76\u51cf\u5c11\u9519\u8bef\uff0c\u5c24\u5176\u662f\u5728\u590d\u6742\u60c5\u51b5\u4e0b\u3002 \u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u5b83\u5c06\u6587\u672c\u63cf\u8ff0\u94fe\u63a5\u5230\u7279\u5b9a\u7684\u56fe\u50cf\u5143\u7d20\uff0c\u5e2e\u52a9\u673a\u5668\u4f7f\u7528\u8bed\u8a00\u548c\u56fe\u50cf\u6765\u89e3\u91ca\u89c6\u89c9\u6548\u679c\u3002\u89c6\u89c9\u57fa\u7840 (VG) \u65e8\u5728\u6839\u636e\u81ea\u7136\u8bed\u8a00\u67e5\u8be2\u5728\u56fe\u50cf\u4e2d\u627e\u5230\u6700\u76f8\u5173\u7684\u5bf9\u8c61\u6216\u533a\u57df<\/p>\n<blockquote><p>\n  DINO\n<\/p><\/blockquote>\n<p>\uff08\u65e0\u6807\u7b7e\u81ea\u84b8\u998f\uff09\u662f Facebook\/metaAI \u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u4e00\u79cd\u81ea\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\u3002 \u5b83\u901a\u8fc7\u6bd4\u8f83\u540c\u4e00\u5e45\u56fe\u50cf\u7684\u4e0d\u540c\u7248\u672c\uff08\u6ca1\u6709\u4eba\u5de5\u6807\u8bb0\u7684\u6570\u636e\uff09\u6765\u81ea\u5b66\uff0c\u4f7f\u7528\u5e08\u751f\u65b9\u6cd5\u6765\u8bc6\u522b\u6a21\u5f0f\u3002<\/p>\n<blockquote><p>\n  Grounding-DINO\n<\/p><\/blockquote>\n<p>Grounding DINO \u6269\u5c55\u4e86 DINO \u7684\u8bed\u8a00\u529f\u80fd\uff0c\u4f7f\u5176\u80fd\u591f\u6839\u636e\u6587\u672c\u63cf\u8ff0\u68c0\u6d4b\u548c\u5b9a\u4f4d\u5bf9\u8c61\u3002\u5b83\u5728\u5f00\u653e\u96c6\u5bf9\u8c61\u68c0\u6d4b\u548c\u57fa\u4e8e\u8bed\u8a00\u7684\u67e5\u8be2\u7b49\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\u3002<\/p>\n<blockquote><p>\n  SAM\n<\/p><\/blockquote>\n<p>SAM \u7531 Meta AI \u5f00\u53d1\uff0c\u53ea\u9700\u5355\u51fb\u6216\u51e0\u4e2a\u70b9\u5373\u53ef\u5206\u5272\u56fe\u50cf\u4e2d\u7684\u4efb\u4f55\u5bf9\u8c61\u3002\u5b83\u5728\u4ece\u7167\u7247\u7f16\u8f91\u5230\u79d1\u5b66\u5206\u6790\u7684\u5e94\u7528\u4e2d\u8868\u73b0\u51fa\u8272\u3002\u6839\u636e\u4ed6\u4eec\u7684 \uff0c\u4ed6\u4eec\u5c1a\u672a\u5b9e\u73b0\u6587\u672c\u4f5c\u4e3a\u8f93\u5165<\/p>\n<p>\u4e3b\u8981\u7279\u70b9\uff1a<\/p>\n<ul>\n<li>\u5bf9\u770b\u4e0d\u89c1\u7684\u7269\u4f53\u8fdb\u884c\u96f6\u6837\u672c\u5206\u5272<\/li>\n<li>\u53ef\u4ee5\u7528\u70b9\u3001\u6846\u6216\u6587\u672c\u63d0\u793a<\/li>\n<li>\u5b9e\u65f6\u751f\u6210\u63a9\u7801<\/li>\n<li>\u5904\u7406\u7269\u4f53\u906e\u6321\u548c\u91cd\u53e0<\/li>\n<\/ul>\n<h2>2\u3001\u6d4b\u8bd5\u6559\u5e08\u6a21\u578b<\/h2>\n<p>\u5728\u6df1\u5165\u7814\u7a76\u6807\u6ce8\u4e4b\u524d\uff0c\u6211\u4eec\u5c06\u9996\u5148\u6d4b\u8bd5\u6211\u4eec\u7684\u6559\u5e08\u6a21\u578b\uff0c\u5373 Grounding DINO \u548c SAM \u7684\u7ec4\u5408\u3002\u8fd9\u4e9b\u6a21\u578b\u5c06\u5145\u5f53\u6211\u4eec\u7684\u201c\u8001\u5e08\u201d\uff0c\u6839\u636e\u6211\u4eec\u63d0\u4f9b\u7684\u63d0\u793a\u81ea\u52a8\u6807\u8bb0\u56fe\u50cf\u3002<\/p>\n<p>\u9996\u5148\u5b89\u88c5\u5fc5\u8981\u7684\u8f6f\u4ef6\u5305\uff1a<\/p>\n<pre><code>!pip3 install autodistill-grounded-sam roboflow autodistill-grounding-dino \n!pip install -U ultralytics<\/code><\/pre>\n<p>\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528\u672c\u4f53\u5b9a\u4e49\u57fa\u7840\u6559\u5e08\u6a21\u578b\uff0c\u5c06\u5bf9\u8c61\u540d\u79f0\uff08\u5982\u201c\u81ea\u884c\u8f66\u201d\u3001\u201c\u4eba\u201d\u7b49\uff09\u6620\u5c04\u5230\u6570\u636e\u96c6\u4e2d\u7684\u7c7b\u3002\u4f8b\u5982\uff1a<\/p>\n<ul>\n<li>\u5b9a\u4e49\u4e00\u4e2a\u672c\u4f53\uff0c\u5c06\u7c7b\u540d\u6620\u5c04\u5230\u6211\u4eec\u7684 GroundingDINO \u63d0\u793a<\/li>\n<li>\u672c\u4f53\u8bcd\u5178\u7684\u683c\u5f0f\u4e3a <code>{caption: class}<\/code>\u3002\u6807\u9898\u5e94\u8be5\u662f\u5bf9\u8c61\u7684\u63cf\u8ff0\uff0c\u4f46\u5728\u6211\u7684\u4f8b\u5b50\u4e2d\uff0c\u53ea\u9700\u8981\u4e00\u4e2a\u8bcd\u5c31\u8db3\u591f\u4e86<\/li>\n<li>\u5176\u4e2d\u6807\u9898\u662f\u53d1\u9001\u7ed9\u57fa\u7840\u6a21\u578b\u7684\u63d0\u793a\uff0c\u800c\u7c7b\u662f\u5c06\u5728\u751f\u6210\u7684\u6807\u6ce8\u4e2d\u4e3a\u8be5\u6807\u9898\u4fdd\u5b58\u7684\u6807\u7b7e<\/li>\n<li>\u6846\u548c\u6587\u672c\u9608\u503c\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u5728\u968f\u673a\u793a\u4f8b\u4e2d\u8fdb\u884c\u6d4b\u8bd5\u6765\u786e\u5b9a\u5b83\u4eec\u7684\u503c\uff0c\u5c31\u50cf\u6211\u505a\u7684\u90a3\u6837<\/li>\n<\/ul>\n<pre><code>from autodistill_grounded_sam import GroundedSAM\nfrom autodistill.detection import CaptionOntology\nfrom autodistill.utils import plot\nimport cv2\n\nbase_model = GroundedSAM(\n      box_threshold=0.4,\n      text_threshold=0.4,\n    ontology=CaptionOntology(\n        {\n            \"Bike\": \"Bike\",\n            \"person\":\"Person\",\n            \"helmet\":\"Helmet\"\n            \n        }\n    )\n)<\/code><\/pre>\n<p>\u73b0\u5728\uff0c\u5728\u968f\u673a\u56fe\u50cf\u4e0a\u6d4b\u8bd5\u6559\u5e08\u6a21\u578b\uff0c\u4ee5\u786e\u4fdd\u5b83\u6b63\u786e\u68c0\u6d4b\u5bf9\u8c61\u3002\u6b64\u6b65\u9aa4\u53ef\u8ba9\u4f60\u5728\u6269\u5927\u89c4\u6a21\u4e4b\u524d\u5fae\u8c03\u9608\u503c\u548c\u63d0\u793a\uff1a<\/p>\n<pre><code>import supervision as sv\nimport matplotlib.pyplot as plt\nfrom collections import Counter\n\npath = \"\/kaggle\/input\/sports-classification\/train\/bmx\/001.jpg\"\n\ndef plot_annotated_image(path):\n    image = cv2.imread(path)\n    predictions = base_model.predict(path)\n\n    annotator = sv.BoxAnnotator()\n    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_LEFT)\n    mask_annotator = sv.MaskAnnotator()\n\n    annotated_image = annotator.annotate(scene=image.copy(), detections=predictions)\n    annotated_image = label_annotator.annotate(annotated_image, detections=predictions)\n    annotated_image = mask_annotator.annotate(annotated_image, detections=predictions)\n    \n    # Count the occurrences of each class\n    class_counts = Counter(predictions.class_id)\n    class_names = {0: 'bikes', 1: 'persons', 2: 'helmets'}\n    \n    # Create the title string\n    title = ', '.join(f\"{count} {class_names[class_id]}\" for class_id, count in class_counts.items())\n    \n    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n    plt.title(title)\n    \n    sv.plot_image(annotated_image, size=(4, 4))\n    \nplot_annotated_image(path)<\/code><\/pre>\n<p>  \u6b64\u56fe\u50cf\u4f7f\u7528 canva \u5236\u4f5c\uff0c\u4e0a\u9762\u51fd\u6570\u4ec5\u7ed8\u5236\u56fe\u50cf\u548c\u5206\u5272\u56fe\u50cf <\/p>\n<h2>3\u3001\u81ea\u52a8\u6807\u6ce8\u6570\u636e\u96c6<\/h2>\n<p>\u4e00\u65e6\u4f60\u5bf9\u8001\u5e08\u611f\u5230\u6ee1\u610f\u6a21\u578b\u7684\u6027\u80fd\uff0c\u662f\u65f6\u5019\u81ea\u52a8\u6807\u6ce8\u7684\u6570\u636e\u96c6\u4e86\u3002\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u65b0\u6587\u4ef6\u5939\u6765\u5b58\u50a8\u6211\u4eec\u6807\u6ce8\u7684\u56fe\u50cf\uff1a<\/p>\n<pre><code>!mkdir datasets # make new folder for the dataset\nbase_model.label(\n  input_folder=\"\/kaggle\/input\/sports-classification\/train\/bmx\",# image folder\n  output_folder=\"\/kaggle\/working\/datasets\"\n)<\/code><\/pre>\n<p>  \u8f93\u51fa <\/p>\n<h2>4\u3001\u4f7f\u7528 Roboflow \u7ec6\u5316\u6807\u7b7e<\/h2>\n<p>\u867d\u7136\u81ea\u52a8\u6807\u7b7e\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u5f00\u59cb\uff0c\u4f46\u5b83\u4eec\u53ef\u80fd\u9700\u8981\u4e00\u4e9b\u4eba\u5de5\u7ec6\u5316\u3002\u8fdb\u5165 Roboflow\uff0c\u8fd9\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u8ba9\u4f60\u8f7b\u677e\u6e05\u7406\u6807\u6ce8\u5e76\u6269\u5145\u6570\u636e\u96c6\u3002<\/p>\n<p>\u521b\u5efa\u4e00\u4e2a Roboflow \u5e10\u6237\u5e76\u8bbe\u7f6e\u4e00\u4e2a\u5de5\u4f5c\u533a\u3002\u7136\u540e\u5c06\u521a\u521a\u6807\u8bb0\u7684\u6570\u636e\u96c6\u4e0a\u4f20\u5230 Roboflow\u3002<\/p>\n<p>\u7136\u540e\uff0c\u83b7\u53d6\u9879\u76ee ID \u548c\u7a7a\u95f4\u5bc6\u94a5\uff1a<\/p>\n<pre><code>import roboflow\nfrom kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nsecret_value_0 = user_secrets.get_secret(\"robo_flow_key\")\n\nrf = roboflow.Roboflow(api_key=secret_value_0)#space APi\n\n# get a workspace\nworkspace = rf.workspace(\"ahmed-haytham\")# name of work space\n\n# Upload data set to a new\/existing project\nworkspace.upload_dataset(\n    \"\/kaggle\/working\/datasets\", # This is your dataset path\n    \"bmx-waxnw\",#project id # This will either create or get a dataset with the given ID\n    num_workers=10,\n    num_retries=0\n)<\/code><\/pre>\n<p>Roboflow \u5141\u8bb8\u4f60\u7f16\u8f91\u6807\u7b7e\u3001\u8c03\u6574\u8fb9\u754c\u6846\u6216\u8499\u7248\uff0c\u4ee5\u53ca\u5e94\u7528\u65cb\u8f6c\u3001\u7ffb\u8f6c\u548c\u566a\u58f0\u7b49\u589e\u5f3a\u529f\u80fd\u3002\u4f60\u8fd8\u53ef\u4ee5\u76f4\u63a5\u5728\u5e73\u53f0\u4e0a\u5c06\u6570\u636e\u96c6\u62c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/p>\n<h2>5\u3001\u4e0b\u8f7d\u6700\u7ec8\u6570\u636e\u96c6<\/h2>\n<p>\u5b8c\u5584\u6807\u7b7e\u540e\uff0c\u4ee5\u8bad\u7ec3\u6240\u9700\u7684\u683c\u5f0f\u4e0b\u8f7d\u6570\u636e\u96c6\u3002<\/p>\n<pre><code>from roboflow import Roboflow\nrf = Roboflow(api_key=secret_value_0) # i canhged this to not make my key public\nproject = rf.workspace(\"ahmed-haytham\").project(\"bmx-waxnw\")\nversion = project.version(2)\ndataset = version.download(\"yolov9\") # roboflow will write it for you<\/code><\/pre>\n<p>\u5982\u6709\u5fc5\u8981\uff0c\u8bf7\u66f4\u65b0\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6\u4e2d\u7684\u8def\u5f84\uff1a<\/p>\n<pre><code>import yaml\n\nwith open('path\/to\/your\/data.yaml') as f:\n    data = yaml.safe_load(f)\n\ndata['train'] = '\/path\/to\/your\/train\/images'\ndata['val'] = '\/path\/to\/your\/val\/images'\n\nwith open('path\/to\/your\/data.yaml', 'w') as f:\n    yaml.dump(data, f)<\/code><\/pre>\n<h2>6\u3001\u7ed3\u675f\u8bed<\/h2>\n<p>\u5c31\u662f\u8fd9\u6837\uff01<\/p>\n<p>\u901a\u8fc7\u4f7f\u7528 Grounding DINO\u3001SAM \u548c AutoDistill \u7b49\u6a21\u578b\/\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u66f4\u8f7b\u677e\u5730\u521b\u5efa\u6570\u636e\u96c6\u3002\u73b0\u5728\uff0c\u4f60\u6709\u4e86\u4e00\u4e2a\u5f3a\u5927\u4e14\u6807\u8bb0\u826f\u597d\u7684\u6570\u636e\u96c6\uff0c\u53ef\u4f9b\u4f60\u8bad\u7ec3\u6a21\u578b\uff01<\/p>\n<hr>\n<p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u8fd9\u4e2a\u6fc0\u52a8\u4eba\u5fc3\u7684\u5192\u9669\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u7814\u7a76\u7528\u4e8e\u7269\u4f53\u68c0\u6d4b\u548c\u56fe\u50cf\u5206\u5272\u7684\u5c0f\u578b\u4f46\u5f3a\u5927\u7684\u6a21\u578b\u7684\u4e16\u754c\u3002\u6211\u4eec\u7684\u76ee\u6807\u662f\u4ec0\u4e48\uff1f\u5229\u7528\u5927\u578b\u6a21\u578b\u7684\u529b\u91cf\u6765\u521b\u5efa\u9ad8\u6548\u3001\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u96c6\uff0c\u8fd9\u4e9b\u6570\u636e\u96c6\u53ef\u4ee5\u8bad\u7ec3\u66f4\u5feb\u3001\u66f4\u5c0f\u7684\u6a21\u578b\uff0c\u800c\u4e0d\u4f1a\u5f71\u54cd\u6027\u80fd\u3002\u8ba9\u6211\u4eec\u5f00\u59cb\u5427\uff01 \u672c\u6587\u9002\u7528\u4e8e\u90a3\u4e9b\u51c6\u5907\u4f7f\u7528 Grounding DINO\u3001SAM \u548c AutoDistill \u7b49\u6700\u5148\u8fdb\u7684\u6a21\u578b\/\u5de5\u5177\u6784\u5efa\u81ea\u5df1\u7684\u6570\u636e\u96c6\u7684\u4eba\u3002\u5982\u679c\u4f60\u66fe\u7ecf\u56e0\u6a21\u578b\u901f\u5ea6\u6162\u6216\u624b\u52a8\u6807\u6ce8\u7684\u9ebb\u70e6\u800c\u611f\u5230\u6cae\u4e27\uff0c\u8bf7\u4e0d\u8981\u5bb3\u6015\uff01\u6211\u4eec\u5c06\u81ea\u52a8\u751f\u6210\u6807\u6ce8\u6570\u636e\u7684\u8fc7\u7a0b\uff0c\u5e76\u4f7f\u7528 Roboflow \u5bf9\u5176\u8fdb\u884c\u6539\u8fdb\u4ee5\u786e\u4fdd\u8d28\u91cf\u3002 1\u3001\u6570\u636e\u96c6\u521b\u5efa\u7b80\u4ecb \u521b\u5efa\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u96c6\u662f\u4efb\u4f55\u6210\u529f\u7684\u673a\u5668\u5b66\u4e60\u9879\u76ee\u7684\u57fa\u7840\u3002\u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u5982\u4f55\u5229\u7528 Grounding DINO \u548c SAM \u7b49\u5927\u578b\u6a21\u578b\u81ea\u52a8\u6807\u8bb0\u56fe\u50cf\u3002\u6211\u4eec\u8fd8\u5c06\u4f7f\u7528 Roboflow \u7b49\u5de5\u5177\u6765\u4f18\u5316\u8fd9\u4e9b\u6807\u7b7e\uff0c\u4ece\u800c\u5b9e\u73b0\u6d41\u7545\u9ad8\u6548\u7684\u5de5\u4f5c\u6d41\u7a0b\u3002 \u5728\u8fd9\u6b21\u5192\u9669\u4e2d\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u5173\u6ce8\u4ee5\u4e0b\u57fa\u672c\u6b65\u9aa4\uff1a Grounding DINO \u7528\u4e8e\u57fa\u4e8e\u6587\u672c\u63d0\u793a\u7684\u81ea\u52a8\u68c0\u6d4b SAM\u6a21\u578b\u7528\u4e8e\u7cbe\u786e\u7684\u56fe\u50cf\u5206\u5272 AutoDistill \u7528\u4e8e\u7b80\u5316\u6570\u636e\u96c6\u521b\u5efa Roboflow \u7528\u4e8e\u6807\u7b7e\u6539\u8fdb\u548c\u589e\u5f3a Grounding Grounding\u5c06 AI \u77e5\u8bc6\u4e0e\u73b0\u5b9e\u4e16\u754c\u7684\u793a\u4f8b\u8054\u7cfb\u8d77\u6765\uff0c\u63d0\u9ad8\u51c6\u786e\u6027\u5e76\u51cf\u5c11\u9519\u8bef\uff0c\u5c24\u5176\u662f\u5728\u590d\u6742\u60c5\u51b5\u4e0b\u3002 \u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u5b83\u5c06\u6587\u672c\u63cf\u8ff0\u94fe\u63a5\u5230\u7279\u5b9a\u7684\u56fe\u50cf\u5143\u7d20\uff0c\u5e2e\u52a9\u673a\u5668\u4f7f\u7528\u8bed\u8a00\u548c\u56fe\u50cf\u6765\u89e3\u91ca\u89c6\u89c9\u6548\u679c\u3002\u89c6\u89c9\u57fa\u7840 (VG) \u65e8\u5728\u6839\u636e\u81ea\u7136\u8bed\u8a00\u67e5\u8be2\u5728\u56fe\u50cf\u4e2d\u627e\u5230\u6700\u76f8\u5173\u7684\u5bf9\u8c61\u6216\u533a\u57df DINO \uff08\u65e0\u6807\u7b7e\u81ea\u84b8\u998f\uff09\u662f Facebook\/metaAI \u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u4e00\u79cd\u81ea\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\u3002 \u5b83\u901a\u8fc7\u6bd4\u8f83\u540c\u4e00\u5e45\u56fe\u50cf\u7684\u4e0d\u540c\u7248\u672c\uff08\u6ca1\u6709\u4eba\u5de5\u6807\u8bb0\u7684\u6570\u636e\uff09\u6765\u81ea\u5b66\uff0c\u4f7f\u7528\u5e08\u751f\u65b9\u6cd5\u6765\u8bc6\u522b\u6a21\u5f0f\u3002 Grounding-DINO Grounding DINO \u6269\u5c55\u4e86 DINO 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[&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-53770","post","type-post","status-publish","format-standard","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/53770","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/comments?post=53770"}],"version-history":[{"count":0,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/posts\/53770\/revisions"}],"wp:attachment":[{"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/media?parent=53770"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/categories?post=53770"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fwq.ai\/blog\/wp-json\/wp\/v2\/tags?post=53770"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}