ZIPyolo-world官方代码，预测 + 训练 6.34MB

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资源文件列表:

YOLO-World.zip 大约有285个文件
  1. YOLO-World/
  2. YOLO-World/demo/
  3. YOLO-World/tools/
  4. YOLO-World/.DS_Store 6KB
  5. __MACOSX/YOLO-World/._.DS_Store 120B
  6. YOLO-World/LICENSE 68.65KB
  7. __MACOSX/YOLO-World/._LICENSE 224B
  8. YOLO-World/Dockerfile 1.18KB
  9. YOLO-World/deploy/
  10. YOLO-World/pyproject.toml 1.48KB
  11. YOLO-World/requirements/
  12. YOLO-World/docs/
  13. YOLO-World/.gitmodules 108B
  14. YOLO-World/README.md 20.11KB
  15. YOLO-World/.dockerignore 15B
  16. YOLO-World/third_party/
  17. YOLO-World/.gitignore 1.37KB
  18. YOLO-World/configs/
  19. YOLO-World/.gitattributes 696B
  20. YOLO-World/yolo_world/
  21. YOLO-World/environment-fromT.txt 7.99KB
  22. __MACOSX/YOLO-World/._environment-fromT.txt 176B
  23. YOLO-World/.git/
  24. YOLO-World/data/
  25. YOLO-World/assets/
  26. YOLO-World/.idea/
  27. __MACOSX/YOLO-World/._.idea 163B
  28. YOLO-World/demo/simple_demo.py 2.27KB
  29. YOLO-World/demo/image_demo.py 7.64KB
  30. YOLO-World/demo/image_prompt_demo.py 12.07KB
  31. YOLO-World/demo/sample_images/
  32. YOLO-World/demo/README.md 2.11KB
  33. YOLO-World/demo/video_demo.py 3.64KB
  34. YOLO-World/demo/gradio_demo.py 9.2KB
  35. YOLO-World/demo/inference.ipynb 1008.85KB
  36. YOLO-World/tools/reparameterize_yoloworld.py 4.55KB
  37. YOLO-World/tools/dist_train.sh 449B
  38. YOLO-World/tools/generate_image_prompts.py 2.2KB
  39. YOLO-World/tools/dist_test.sh 479B
  40. YOLO-World/tools/test.py 5.32KB
  41. YOLO-World/tools/generate_text_prompts.py 1.15KB
  42. YOLO-World/tools/train.py 4.12KB
  43. YOLO-World/deploy/__init__.py
  44. YOLO-World/deploy/onnx_demo.py 7.56KB
  45. YOLO-World/deploy/easydeploy/
  46. YOLO-World/deploy/export_onnx.py 7.13KB
  47. YOLO-World/deploy/tflite_demo.py 8.15KB
  48. YOLO-World/requirements/demo_requirements.txt 26B
  49. __MACOSX/YOLO-World/requirements/._demo_requirements.txt 280B
  50. YOLO-World/requirements/onnx_requirements.txt 36B
  51. __MACOSX/YOLO-World/requirements/._onnx_requirements.txt 280B
  52. YOLO-World/requirements/basic_requirements.txt 161B
  53. __MACOSX/YOLO-World/requirements/._basic_requirements.txt 323B
  54. YOLO-World/docs/tflite_deploy.md 2.42KB
  55. YOLO-World/docs/data.md 5.16KB
  56. YOLO-World/docs/faq.md 512B
  57. YOLO-World/docs/deploy.md 2.2KB
  58. YOLO-World/docs/reparameterize.md 3.01KB
  59. YOLO-World/docs/finetuning.md 3.51KB
  60. YOLO-World/docs/installation.md 1.39KB
  61. YOLO-World/docs/updates.md 731B
  62. YOLO-World/docs/prompt_yolo_world.md 3.29KB
  63. YOLO-World/third_party/mmyolo/
  64. YOLO-World/configs/finetune_coco/
  65. YOLO-World/configs/segmentation/
  66. YOLO-World/configs/image_prompts/
  67. YOLO-World/configs/pretrain/
  68. YOLO-World/configs/pretrain_v1/
  69. YOLO-World/configs/prompt_tuning_coco/
  70. YOLO-World/yolo_world/version.py 744B
  71. YOLO-World/yolo_world/datasets/
  72. YOLO-World/yolo_world/__init__.py 342B
  73. YOLO-World/yolo_world/models/
  74. YOLO-World/yolo_world/engine/
  75. YOLO-World/.git/config 313B
  76. YOLO-World/.git/objects/
  77. YOLO-World/.git/HEAD 23B
  78. YOLO-World/.git/info/
  79. YOLO-World/.git/logs/
  80. YOLO-World/.git/description 73B
  81. YOLO-World/.git/hooks/
  82. YOLO-World/.git/refs/
  83. YOLO-World/.git/index 19.98KB
  84. YOLO-World/.git/packed-refs 181B
  85. YOLO-World/data/texts/
  86. YOLO-World/assets/reparameterize.png 62.77KB
  87. YOLO-World/assets/finetune_yoloworld.png 466.43KB
  88. YOLO-World/assets/yolo_arch.png 297.76KB
  89. YOLO-World/assets/yolo_logo.png 99.93KB
  90. YOLO-World/.idea/inspectionProfiles/
  91. __MACOSX/YOLO-World/.idea/._inspectionProfiles 163B
  92. YOLO-World/.idea/vcs.xml 167B
  93. __MACOSX/YOLO-World/.idea/._vcs.xml 163B
  94. YOLO-World/.idea/.gitignore 176B
  95. __MACOSX/YOLO-World/.idea/._.gitignore 163B
  96. YOLO-World/.idea/workspace.xml 4.71KB
  97. __MACOSX/YOLO-World/.idea/._workspace.xml 163B
  98. YOLO-World/.idea/YOLO-World.iml 470B
  99. __MACOSX/YOLO-World/.idea/._YOLO-World.iml 163B
  100. YOLO-World/.idea/modules.xml 272B
  101. __MACOSX/YOLO-World/.idea/._modules.xml 163B
  102. YOLO-World/.idea/misc.xml 264B
  103. __MACOSX/YOLO-World/.idea/._misc.xml 163B
  104. YOLO-World/demo/sample_images/zidane.jpg 164.99KB
  105. YOLO-World/demo/sample_images/bus.jpg 476.01KB
  106. YOLO-World/deploy/easydeploy/bbox_code/
  107. YOLO-World/deploy/easydeploy/tools/
  108. YOLO-World/deploy/easydeploy/docs/
  109. YOLO-World/deploy/easydeploy/README_zh-CN.md 406B
  110. YOLO-World/deploy/easydeploy/README.md 464B
  111. YOLO-World/deploy/easydeploy/onnx_demo.py
  112. YOLO-World/deploy/easydeploy/examples/
  113. YOLO-World/deploy/easydeploy/model/
  114. YOLO-World/deploy/easydeploy/nms/
  115. YOLO-World/deploy/easydeploy/deepstream/
  116. YOLO-World/deploy/easydeploy/backbone/
  117. YOLO-World/configs/finetune_coco/yolo_world_v2_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.03KB
  118. YOLO-World/configs/finetune_coco/yolo_world_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.36KB
  119. YOLO-World/configs/finetune_coco/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.03KB
  120. YOLO-World/configs/finetune_coco/yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.05KB
  121. YOLO-World/configs/finetune_coco/yolo_world_v2_xl_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 7.18KB
  122. YOLO-World/configs/finetune_coco/README.md 3.7KB
  123. YOLO-World/configs/finetune_coco/yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
  124. YOLO-World/configs/finetune_coco/yolo_world_v2_l_vlpan_bn_sgd_1e-3_80e_8gpus_mask-refine_finetune_coco.py 6.8KB
  125. YOLO-World/configs/finetune_coco/yolo_world_v2_m_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.02KB
  126. YOLO-World/configs/finetune_coco/yolo_world_v2_s_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 5.99KB
  127. YOLO-World/configs/finetune_coco/yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_finetune_coco.py 6.64KB
  128. YOLO-World/configs/finetune_coco/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.04KB
  129. YOLO-World/configs/finetune_coco/yolo_world_l_dual_vlpan_2e-4_80e_8gpus_finetune_coco.py 5.89KB
  130. YOLO-World/configs/finetune_coco/yolo_world_l_dual_vlpan_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6.05KB
  131. YOLO-World/configs/segmentation/yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py 8.51KB
  132. YOLO-World/configs/segmentation/yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py 9.06KB
  133. YOLO-World/configs/segmentation/yolo_world_v2_seg_l_vlpan_bn_2e-4_80e_8gpus_seghead_finetune_lvis.py 9.08KB
  134. YOLO-World/configs/segmentation/README.md 2.97KB
  135. YOLO-World/configs/segmentation/yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py 9.06KB
  136. YOLO-World/configs/segmentation/yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py 8.41KB
  137. YOLO-World/configs/segmentation/yolo_world_v2_seg_m_vlpan_bn_2e-4_80e_8gpus_seghead_finetune_lvis.py 9.08KB
  138. YOLO-World/configs/image_prompts/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_image_prompt_demo.py 5.35KB
  139. YOLO-World/configs/pretrain/yolo_world_v2_m_vlpan_bn_noeinsum_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.86KB
  140. YOLO-World/configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_val.py 6.72KB
  141. YOLO-World/configs/pretrain/yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.74KB
  142. YOLO-World/configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7.58KB
  143. YOLO-World/configs/pretrain/yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.76KB
  144. YOLO-World/configs/pretrain/yolo_world_v2_xl_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7.07KB
  145. YOLO-World/configs/pretrain/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7.5KB
  146. YOLO-World/configs/pretrain/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.76KB
  147. YOLO-World/configs/pretrain/yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7.49KB
  148. YOLO-World/configs/pretrain/yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7.38KB
  149. YOLO-World/configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.76KB
  150. YOLO-World/configs/pretrain/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_cc3mlite_train_lvis_minival.py 7.24KB
  151. YOLO-World/configs/pretrain/yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_800ft_lvis_minival.py 7.49KB
  152. YOLO-World/configs/pretrain/yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.66KB
  153. YOLO-World/configs/pretrain_v1/yolo_world_x_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.78KB
  154. YOLO-World/configs/pretrain_v1/yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_val.py 6.73KB
  155. YOLO-World/configs/pretrain_v1/yolo_world_m_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.78KB
  156. YOLO-World/configs/pretrain_v1/yolo_world_s_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.78KB
  157. YOLO-World/configs/pretrain_v1/yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 6.78KB
  158. YOLO-World/configs/pretrain_v1/README.md 3.63KB
  159. YOLO-World/configs/prompt_tuning_coco/READEME.md 534B
  160. YOLO-World/configs/prompt_tuning_coco/yolo_world_v2_l_vlpan_bn_sgd_1e-3_80e_8gpus_all_finetuning_coco.py 4.58KB
  161. YOLO-World/configs/prompt_tuning_coco/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_prompt_tuning_coco.py 5.11KB
  162. YOLO-World/configs/prompt_tuning_coco/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_prompt_tuning_coco.py 6.82KB
  163. YOLO-World/yolo_world/datasets/yolov5_obj365v1.py 508B
  164. YOLO-World/yolo_world/datasets/yolov5_cc3m_grounding.py 7.05KB
  165. YOLO-World/yolo_world/datasets/mm_dataset.py 3.99KB
  166. YOLO-World/yolo_world/datasets/__init__.py 786B
  167. YOLO-World/yolo_world/datasets/transformers/
  168. YOLO-World/yolo_world/datasets/utils.py 2.1KB
  169. YOLO-World/yolo_world/datasets/yolov5_lvis.py 491B
  170. YOLO-World/yolo_world/datasets/yolov5_v3det.py 3.75KB
  171. YOLO-World/yolo_world/datasets/yolov5_mixed_grounding.py 7.26KB
  172. YOLO-World/yolo_world/datasets/yolov5_obj365v2.py 508B
  173. YOLO-World/yolo_world/models/losses/
  174. YOLO-World/yolo_world/models/dense_heads/
  175. YOLO-World/yolo_world/models/layers/
  176. YOLO-World/yolo_world/models/necks/
  177. YOLO-World/yolo_world/models/__init__.py 314B
  178. YOLO-World/yolo_world/models/data_preprocessors/
  179. YOLO-World/yolo_world/models/backbones/
  180. YOLO-World/yolo_world/models/detectors/
  181. YOLO-World/yolo_world/models/assigner/
  182. YOLO-World/yolo_world/engine/__init__.py 84B
  183. YOLO-World/yolo_world/engine/optimizers/
  184. YOLO-World/.git/objects/pack/
  185. YOLO-World/.git/objects/info/
  186. YOLO-World/.git/info/exclude 240B
  187. YOLO-World/.git/logs/HEAD 183B
  188. YOLO-World/.git/logs/refs/
  189. YOLO-World/.git/hooks/commit-msg.sample 896B
  190. YOLO-World/.git/hooks/pre-rebase.sample 4.78KB
  191. YOLO-World/.git/hooks/pre-commit.sample 1.6KB
  192. YOLO-World/.git/hooks/applypatch-msg.sample 478B
  193. YOLO-World/.git/hooks/fsmonitor-watchman.sample 4.62KB
  194. YOLO-World/.git/hooks/pre-receive.sample 544B
  195. YOLO-World/.git/hooks/prepare-commit-msg.sample 1.46KB
  196. YOLO-World/.git/hooks/post-update.sample 189B
  197. YOLO-World/.git/hooks/pre-merge-commit.sample 416B
  198. YOLO-World/.git/hooks/pre-applypatch.sample 424B
  199. YOLO-World/.git/hooks/pre-push.sample 1.34KB
  200. YOLO-World/.git/hooks/update.sample 3.56KB
  201. YOLO-World/.git/hooks/push-to-checkout.sample 2.72KB
  202. YOLO-World/.git/refs/heads/
  203. YOLO-World/.git/refs/tags/
  204. YOLO-World/.git/refs/remotes/
  205. YOLO-World/data/texts/coco_class_texts.json 1022B
  206. YOLO-World/data/texts/lvis_v1_base_class_captions.json 20.13KB
  207. YOLO-World/data/texts/obj365v1_class_texts.json 4.92KB
  208. YOLO-World/data/texts/lvis_v1_class_texts.json 27.51KB
  209. YOLO-World/.idea/inspectionProfiles/profiles_settings.xml 174B
  210. __MACOSX/YOLO-World/.idea/inspectionProfiles/._profiles_settings.xml 163B
  211. YOLO-World/.idea/inspectionProfiles/Project_Default.xml 658B
  212. __MACOSX/YOLO-World/.idea/inspectionProfiles/._Project_Default.xml 163B
  213. YOLO-World/deploy/easydeploy/bbox_code/bbox_coder.py 1.57KB
  214. YOLO-World/deploy/easydeploy/bbox_code/__init__.py 240B
  215. YOLO-World/deploy/easydeploy/tools/build_engine.py 4.89KB
  216. YOLO-World/deploy/easydeploy/tools/image-demo.py 4.84KB
  217. YOLO-World/deploy/easydeploy/tools/export_onnx.py 5.27KB
  218. YOLO-World/deploy/easydeploy/docs/model_convert.md 6.28KB
  219. YOLO-World/deploy/easydeploy/examples/config.py 3.17KB
  220. YOLO-World/deploy/easydeploy/examples/requirements.txt 36B
  221. __MACOSX/YOLO-World/deploy/easydeploy/examples/._requirements.txt 224B
  222. YOLO-World/deploy/easydeploy/examples/preprocess.py 2.08KB
  223. YOLO-World/deploy/easydeploy/examples/main_onnxruntime.py 3.65KB
  224. YOLO-World/deploy/easydeploy/examples/cv2_nms.py 1.2KB
  225. YOLO-World/deploy/easydeploy/examples/numpy_coder.py 10.8KB
  226. YOLO-World/deploy/easydeploy/model/backendwrapper.py 6.72KB
  227. YOLO-World/deploy/easydeploy/model/backend.py 493B
  228. YOLO-World/deploy/easydeploy/model/__init__.py 237B
  229. YOLO-World/deploy/easydeploy/model/model.py 8.72KB
  230. YOLO-World/deploy/easydeploy/nms/trt_nms.py 7.86KB
  231. YOLO-World/deploy/easydeploy/nms/__init__.py 182B
  232. YOLO-World/deploy/easydeploy/nms/ort_nms.py 8.54KB
  233. YOLO-World/deploy/easydeploy/deepstream/CMakeLists.txt 1.06KB
  234. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/._CMakeLists.txt 224B
  235. YOLO-World/deploy/easydeploy/deepstream/deepstream_app_config.txt 870B
  236. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/._deepstream_app_config.txt 224B
  237. YOLO-World/deploy/easydeploy/deepstream/custom_mmyolo_bbox_parser/
  238. YOLO-World/deploy/easydeploy/deepstream/README_zh-CN.md 1.59KB
  239. YOLO-World/deploy/easydeploy/deepstream/coco_labels.txt 625B
  240. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/._coco_labels.txt 224B
  241. YOLO-World/deploy/easydeploy/deepstream/README.md 1.88KB
  242. YOLO-World/deploy/easydeploy/deepstream/configs/
  243. YOLO-World/deploy/easydeploy/backbone/__init__.py 199B
  244. YOLO-World/deploy/easydeploy/backbone/common.py 444B
  245. YOLO-World/deploy/easydeploy/backbone/focus.py 2.77KB
  246. YOLO-World/yolo_world/datasets/transformers/mm_mix_img_transforms.py 46.38KB
  247. YOLO-World/yolo_world/datasets/transformers/__init__.py 383B
  248. YOLO-World/yolo_world/datasets/transformers/mm_transforms.py 4.57KB
  249. YOLO-World/yolo_world/models/losses/__init__.py 113B
  250. YOLO-World/yolo_world/models/losses/dynamic_loss.py 1.26KB
  251. YOLO-World/yolo_world/models/dense_heads/yolo_world_seg_head.py 24.07KB
  252. YOLO-World/yolo_world/models/dense_heads/__init__.py 346B
  253. YOLO-World/yolo_world/models/dense_heads/yolo_world_head.py 29.37KB
  254. YOLO-World/yolo_world/models/layers/yolo_bricks.py 24.13KB
  255. YOLO-World/yolo_world/models/layers/__init__.py 585B
  256. YOLO-World/yolo_world/models/necks/yolo_world_pafpn.py 9.61KB
  257. YOLO-World/yolo_world/models/necks/__init__.py 167B
  258. YOLO-World/yolo_world/models/data_preprocessors/__init__.py 146B
  259. YOLO-World/yolo_world/models/data_preprocessors/data_preprocessor.py 2.28KB
  260. YOLO-World/yolo_world/models/backbones/__init__.py 458B
  261. YOLO-World/yolo_world/models/backbones/mm_backbone.py 8.26KB
  262. YOLO-World/yolo_world/models/detectors/__init__.py 256B
  263. YOLO-World/yolo_world/models/detectors/yolo_world_image.py 11.02KB
  264. YOLO-World/yolo_world/models/detectors/yolo_world.py 9.04KB
  265. YOLO-World/yolo_world/models/assigner/task_aligned_assigner.py 4.28KB
  266. YOLO-World/yolo_world/models/assigner/__init__.py 91B
  267. YOLO-World/yolo_world/engine/optimizers/__init__.py 161B
  268. YOLO-World/yolo_world/engine/optimizers/yolow_v5_optim_constructor.py 8.45KB
  269. YOLO-World/.git/objects/pack/pack-020ae3c8b9454f103737f1162819ba569fe292d4.idx 32.44KB
  270. YOLO-World/.git/objects/pack/pack-020ae3c8b9454f103737f1162819ba569fe292d4.pack 3.9MB
  271. YOLO-World/.git/logs/refs/heads/
  272. YOLO-World/.git/logs/refs/remotes/
  273. YOLO-World/.git/refs/heads/master 41B
  274. YOLO-World/.git/refs/remotes/origin/
  275. YOLO-World/deploy/easydeploy/deepstream/custom_mmyolo_bbox_parser/nvdsparsebbox_mmyolo.cpp 3.63KB
  276. YOLO-World/deploy/easydeploy/deepstream/configs/config_infer_rtmdet.txt 479B
  277. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/configs/._config_infer_rtmdet.txt 224B
  278. YOLO-World/deploy/easydeploy/deepstream/configs/config_infer_yolov8.txt 453B
  279. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/configs/._config_infer_yolov8.txt 224B
  280. YOLO-World/deploy/easydeploy/deepstream/configs/config_infer_yolov5.txt 453B
  281. __MACOSX/YOLO-World/deploy/easydeploy/deepstream/configs/._config_infer_yolov5.txt 224B
  282. YOLO-World/.git/logs/refs/heads/master 183B
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资源介绍:

YOLO（You Only Look Once）是一种在计算机视觉和机器学习领域广受欢迎的目标检测系统。YOLO模型以其高效性、速度和准确性在实时目标检测任务中表现突出，被广泛应用于自动驾驶、监控系统、视频分析等领域。YOLO的核心思想是在单一神经网络中直接预测边界框和概率，这与传统的目标检测方法不同，后者通常采用多步骤流程，例如选择性搜索选取候选区域，再对这些区域进行分类和边界框回归。 YOLO模型的一个显著特点是它在检测速度和准确率之间的出色权衡。与基于区域的方法相比，YOLO在处理图像时速度更快，因为它只需要一个单一的神经网络评估，而不需要通过复杂的管道。这使得YOLO非常适合于对实时性要求高的应用。此外，YOLO对图像中的目标位置、大小和外观特征进行联合推理，从而提供更加精确的检测结果。 YOLO模型自2015年首次提出以来，已经发展出多个版本，如YOLOv2、YOLOv3、YOLOv4，以及YOLOv5和最新的YOLOv7，每一个新版本都在前一版本的基础上进行了改进，增强了模型的性能。改进的方面包括但不限于网络结构的创新、锚框策略的优化、损失函数的调整、数据增强技术的使用等。每一代YOLO模型的更新都进一步提高了模型在各类数据集上的检测精度，同时也在不断减少对计算资源的需求，使得YOLO能够运行在更多类型的设备上。 YOLO官方代码库通常包含用于训练模型的完整流程和用于预测的代码。这意味着开发者可以直接使用这些代码来训练自己的目标检测模型，或者用已经训练好的模型进行目标检测。官方代码库通常支持多种后端，如Darknet、PyTorch、TensorFlow等，为不同背景的开发者提供了灵活性。同时，代码库中还包含数据预处理、模型配置、训练脚本、评估脚本、预测脚本和可视化工具等，构成了一个完整的生态系统。 从文件名称列表中我们仅看到了"YOLO-World"，这暗示该压缩包可能包含与YOLO相关的代码、文档和可能的模型文件。开发者通过这些资源可以深入了解YOLO模型的工作原理，也可以根据自己的需求进行模型的微调或者应用开发。YOLO社区提供了大量的预训练模型和开源资源，极大促进了目标检测技术在各个领域的应用。 对于希望利用YOLO进行目标检测研究或者应用开发的研究者和工程师来说，官方代码库是一个宝贵的资源。它不仅提供了一个强大的目标检测框架，还通过开放的代码和活跃的社区支持，鼓励了技术创新和知识分享。通过阅读和使用官方代码，开发者可以更快地融入这一领域的最新进展，为自己的项目带来最先进的技术解决方案。
<div align="center"> <img src="./assets/yolo_logo.png"> <br> <a href="https://scholar.google.com/citations?hl=zh-CN&user=PH8rJHYAAAAJ">Tianheng Cheng</a><sup><span>2,3,*</span></sup>, <a href="https://linsong.info/">Lin Song</a><sup><span>1,📧,*</span></sup>, <a href="https://yxgeee.github.io/">Yixiao Ge</a><sup><span>1,🌟,2</span></sup>, <a href="http://eic.hust.edu.cn/professor/liuwenyu/"> Wenyu Liu</a><sup><span>3</span></sup>, <a href="https://xwcv.github.io/">Xinggang Wang</a><sup><span>3,📧</span></sup>, <a href="https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en">Ying Shan</a><sup><span>1,2</span></sup> </br> \* Equal contribution 🌟 Project lead 📧 Corresponding author <sup>1</sup> Tencent AI Lab, <sup>2</sup> ARC Lab, Tencent PCG <sup>3</sup> Huazhong University of Science and Technology <br> <div> [![arxiv paper](https://img.shields.io/badge/Project-Page-green)](https://wondervictor.github.io/) [![arxiv paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2401.17270) <a href="https://colab.research.google.com/github/AILab-CVC/YOLO-World/blob/master/inference.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![demo](https://img.shields.io/badge/🤗HugginngFace-Spaces-orange)](https://huggingface.co/spaces/stevengrove/YOLO-World) [![Replicate](https://replicate.com/zsxkib/yolo-world/badge)](https://replicate.com/zsxkib/yolo-world) [![hfpaper](https://img.shields.io/badge/🤗HugginngFace-Paper-yellow)](https://huggingface.co/papers/2401.17270) [![license](https://img.shields.io/badge/License-GPLv3.0-blue)](LICENSE) [![yoloworldseg](https://img.shields.io/badge/YOLOWorldxEfficientSAM-🤗Spaces-orange)](https://huggingface.co/spaces/SkalskiP/YOLO-World) [![yologuide](https://img.shields.io/badge/📖Notebook-roboflow-purple)](https://supervision.roboflow.com/develop/notebooks/zero-shot-object-detection-with-yolo-world) [![deploy](https://media.roboflow.com/deploy.svg)](https://inference.roboflow.com/foundation/yolo_world/) </div> </div> ## Notice **YOLO-World is still under active development!** We recommend that everyone **use English to communicate on issues**, as this helps developers from around the world discuss, share experiences, and answer questions together. For business licensing and other related inquiries, don't hesitate to contact `yixiaoge@tencent.com`. ## 🔥 Updates `[2024-11-5]`: We update the `YOLO-World-Image` and you can try it at HuggingFace [YOLO-World-Image (Preview Version)](https://huggingface.co/spaces/wondervictor/YOLO-World-Image). It's a *preview* version and we are still improving it! Detailed documents about training and few-shot inference are coming soon.\ `[2024-7-8]`: YOLO-World now has been integrated into [ComfyUI](https://github.com/StevenGrove/ComfyUI-YOLOWorld)! Come and try adding YOLO-World to your workflow now! You can access it at [StevenGrove/ComfyUI-YOLOWorld](https://github.com/StevenGrove/ComfyUI-YOLOWorld)! `[2024-5-18]:` YOLO-World models have been [integrated with the FiftyOne computer vision toolkit](https://docs.voxel51.com/integrations/ultralytics.html#open-vocabulary-detection) for streamlined open-vocabulary inference across image and video datasets. `[2024-5-16]:` Hey guys! Long time no see! This update contains (1) [fine-tuning guide](https://github.com/AILab-CVC/YOLO-World?#highlights--introduction) and (2) [TFLite Export](./docs/tflite_deploy.md) with INT8 Quantization. `[2024-5-9]:` This update contains the real [`reparameterization`](./docs/reparameterize.md) 🪄, and it's better for fine-tuning on custom datasets and improves the training/inference efficiency 🚀! `[2024-4-28]:` Long time no see! This update contains bugfixs and improvements: (1) ONNX demo; (2) image demo (support tensor input); (2) new pre-trained models; (3) image prompts; (4) simple version for fine-tuning / deployment; (5) guide for installation (include a `requirements.txt`). `[2024-3-28]:` We provide: (1) more high-resolution pre-trained models (e.g., S, M, X) ([#142](https://github.com/AILab-CVC/YOLO-World/issues/142)); (2) pre-trained models with CLIP-Large text encoders. Most importantly, we preliminarily fix the **fine-tuning without `mask-refine`** and explore a new fine-tuning setting ([#160](https://github.com/AILab-CVC/YOLO-World/issues/160),[#76](https://github.com/AILab-CVC/YOLO-World/issues/76)). In addition, fine-tuning YOLO-World with `mask-refine` also obtains significant improvements, check more details in [configs/finetune_coco](./configs/finetune_coco/). `[2024-3-16]:` We fix the bugs about the demo ([#110](https://github.com/AILab-CVC/YOLO-World/issues/110),[#94](https://github.com/AILab-CVC/YOLO-World/issues/94),[#129](https://github.com/AILab-CVC/YOLO-World/issues/129), [#125](https://github.com/AILab-CVC/YOLO-World/issues/125)) with visualizations of segmentation masks, and release [**YOLO-World with Embeddings**](./docs/prompt_yolo_world.md), which supports prompt tuning, text prompts and image prompts. `[2024-3-3]:` We add the **high-resolution YOLO-World**, which supports `1280x1280` resolution with higher accuracy and better performance for small objects! `[2024-2-29]:` We release the newest version of [ **YOLO-World-v2**](./docs/updates.md) with higher accuracy and faster speed! We hope the community can join us to improve YOLO-World! `[2024-2-28]:` Excited to announce that YOLO-World has been accepted by **CVPR 2024**! We're continuing to make YOLO-World faster and stronger, as well as making it better to use for all. `[2024-2-22]:` We sincerely thank [RoboFlow](https://roboflow.com/) and [@Skalskip92](https://twitter.com/skalskip92) for the [**Video Guide**](https://www.youtube.com/watch?v=X7gKBGVz4vs) about YOLO-World, nice work! `[2024-2-18]:` We thank [@Skalskip92](https://twitter.com/skalskip92) for developing the wonderful segmentation demo via connecting YOLO-World and EfficientSAM. You can try it now at the [🤗 HuggingFace Spaces](https://huggingface.co/spaces/SkalskiP/YOLO-World). `[2024-2-17]:` The largest model **X** of YOLO-World is released, which achieves better zero-shot performance! `[2024-2-17]:` We release the code & models for **YOLO-World-Seg** now! YOLO-World now supports open-vocabulary / zero-shot object segmentation! `[2024-2-15]:` The pre-traind YOLO-World-L with CC3M-Lite is released! `[2024-2-14]:` We provide the [`image_demo`](demo.py) for inference on images or directories. `[2024-2-10]:` We provide the [fine-tuning](./docs/finetuning.md) and [data](./docs/data.md) details for fine-tuning YOLO-World on the COCO dataset or the custom datasets! `[2024-2-3]:` We support the `Gradio` demo now in the repo and you can build the YOLO-World demo on your own device! `[2024-2-1]:` We've released the code and weights of YOLO-World now! `[2024-2-1]:` We deploy the YOLO-World demo on [HuggingFace 🤗](https://huggingface.co/spaces/stevengrove/YOLO-World), you can try it now! `[2024-1-31]:` We are excited to launch **YOLO-World**, a cutting-edge real-time open-vocabulary object detector. ## TODO YOLO-World is under active development and please stay tuned ☕️! If you have suggestions📃 or ideas💡,**we would love for you to bring them up in the [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109)** ❤️! > YOLO-World 目前正在积极开发中📃，如果你有建议或者想法💡，**我们非常希望您在 [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109) 中提出来** ❤️！ ## [FAQ (Frequently Asked Questions)](https://github.com/AILab-CVC/YOLO-World/discussions/149) We have set up an FAQ about YOLO-World in the discussion on GitHub. We hope everyone can raise issues or solutions during use here, and we also hope that everyone can quickly find solutions from it. > 我们在GitHub的discussion中建立了关于YOLO-World的常见问答，这里将收集
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