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dataset1/README.dataset.txt

@@ -1,6 +0,0 @@
-# huxianfeng > 2025-09-27 7:15pm
-https://universe.roboflow.com/crabapples/huxianfeng-lrfxc
-
-Provided by a Roboflow user
-License: CC BY 4.0
-

dataset1/README.roboflow.txt

@@ -1,27 +0,0 @@
-
-huxianfeng - v1 2025-09-27 7:15pm
-==============================
-
-This dataset was exported via roboflow.com on September 27, 2025 at 11:15 AM GMT
-
-Roboflow is an end-to-end computer vision platform that helps you
-* collaborate with your team on computer vision projects
-* collect & organize images
-* understand and search unstructured image data
-* annotate, and create datasets
-* export, train, and deploy computer vision models
-* use active learning to improve your dataset over time
-
-For state of the art Computer Vision training notebooks you can use with this dataset,
-visit https://github.com/roboflow/notebooks
-
-To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
-
-The dataset includes 112 images.
-Hu-QGXy are annotated in YOLOv8 format.
-
-The following pre-processing was applied to each image:
-
-No image augmentation techniques were applied.
-
-

dataset1/data.yaml

@@ -1,16 +0,0 @@
-train: ../train/images
-val: ../valid/images
-test: ../test/images
-
-kpt_shape: [7, 3]
-flip_idx: [0, 1, 2, 3, 4, 5, 6]
-
-nc: 1
-names: ['tiger']
-
-roboflow:
-  workspace: crabapples
-  project: huxianfeng-lrfxc
-  version: 1
-  license: CC BY 4.0
-  url: https://universe.roboflow.com/crabapples/huxianfeng-lrfxc/dataset/1

dataset1/train.yaml

@@ -2,8 +2,11 @@ path: ./dataset1 # 数据集根目录
 train: train/images # 训练集目录名
 val: valid/images # 验证集目录名
 task: pose #任务类型为 pose
-kpt_shape: [7, 3]
+nc: 2 # 类别数
-flip_idx: [0, 1, 2, 3, 4, 5, 6]
+loss: CIou
-
+names:
-nc: 1
+  0: 'left_hand'
-names: ['tiger']
+  1: 'right_hand'
+kpt_shape: [21, 3] # 关键点数量, 维度数量 (2 表示 x,y 或 3 表示 x,y,可见性)
+# 关键点
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

dataset1/train/labels/11_png.rf.84f9ee8084a53577894c6cbc4f91f202.txt

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+1 0.5346023185483871 0.5052433057851239 0.8918351814516129 0.8641475206611571 0.1505796370967742 0.4166020661157025 2 0.22422217741935485 0.4842243801652893 2 0.6818086693548387 0.7208914049586777 2 0.29626330645161286 0.3376000826446281 2 0.23784022177419356 0.2299414049586777 2 0.34631875 0.13824677685950415 2 0.5484632056451614 0.21867471074380165 2 0.5044719758064516 0.12376785123966941 2 0.8889540322580645 0.4377098347107438 2 0.8561526209677419 0.3283025619834711 2 0.8716082661290323 0.5609719008264462 2 0.5870334677419355 0.3139796694214876 2 0.3939064516129032 0.2332880991735537 2 0.45001290322580645 0.33698669421487604 2 0.36036995967741936 0.4285145454545455 2 0.30113961693548386 0.5454384297520661 2 0.8309510080645162 0.6225948760330579 2 0.6104565524193548 0.41439611570247936 2 0.5067165322580646 0.4462917355371901 2 0.4136083669354839 0.518783388429752 2 0.3705227822580645 0.5853779338842976 2

dataset1/train/labels/13_png.rf.3013e5f6f9720e6268ffb611134c6b36.txt

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dataset1/train/labels/14_png.rf.aad2883f0dbd6bebb5e7b25e3ebeb14c.txt

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dataset1/train/labels/15_png.rf.206d8389a6ec6a30bff0dba9f4bfd973.txt

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dataset1/train/labels/16_png.rf.f729b7c5d8f7bf555fe334a6465e34d3.txt

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+1 0.5 0.5364312091503268 1 0.7863402777777778 0.5649639225181597 0.19916339869281047 2 0.6479125907990314 0.23460367647058825 2 0.8791111380145279 0.7245125816993464 2 0.4134193704600484 0.20253031045751635 2 0.3181670702179177 0.17295686274509803 2 0.15972590799031477 0.16880808823529414 2 0.11854322033898305 0.25573063725490197 2 0.048607384987893465 0.1953497549019608 2 0.2647043583535109 0.5706348039215686 2 0.2264537530266344 0.44693692810457514 2 0.4511418886198547 0.7194489379084967 2 0.2855991525423729 0.35522728758169936 2 0.24243026634382567 0.2022484477124183 2 0.4071611380145278 0.27347475490196077 2 0.5661698547215496 0.27759689542483656 2 0.7602290556900727 0.31224468954248363 2 0.6525865617433415 0.7442246732026144 2 0.5211013317191283 0.548598366013072 2 0.5995731234866828 0.4335383986928104 2 0.7480989104116224 0.4293311274509804 2 0.9000848668280872 0.4563567810457516 2

dataset1/train/labels/17_png.rf.a15d91cf868401368a87d49b73dc9621.txt

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dataset1/train/labels/2_png.rf.6587ab60765f5052e420bb69c41e54bc.txt

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dataset1/train/labels/4_png.rf.75abf58ec47fa51b9dc23c10643748fc.txt

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dataset1/train/labels/5_png.rf.f7ce73c04c2ea96d58a2b23afc320cc6.txt

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dataset1/train/labels/6_png.rf.6d04b55c7f6a21d5b96ecc4d5fbe9c62.txt

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dataset1/train/labels/8_png.rf.b2093ef03bffa0e11bde4904c2162044.txt

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dataset1/train/labels/9_png.rf.43d17f7e040e179852c0592eea6d79b2.txt

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dataset1/valid/labels/10_png.rf.46d953fd2983a92de48a83982f59bf33.txt

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dataset1/valid/labels/12_png.rf.7ff052ef3051396d66b2543da25bbf41.txt

@@ -0,0 +1 @@
+0 0.4934180995475113 0.5425261904761906 0.9205832579185521 0.9149477011494254 0.8756731900452489 0.6103114942528736 2 0.7839160633484163 0.6851117405582923 2 0.2934842760180995 0.7704527914614122 2 0.7557165158371041 0.30534285714285714 2 0.8009506787330316 0.23459950738916258 2 0.6566240950226244 0.22007668308702794 2 0.6899789592760182 0.1228159277504105 2 0.5204209276018099 0.23525164203612478 2 0.5548951357466063 0.15799786535303775 2 0.3282899321266968 0.3079405582922824 2 0.34039762443438915 0.2312694581280788 2 0.6401565610859729 0.7991616584564861 2 0.7107548642533937 0.4065536124794745 2 0.5975443438914027 0.3528559934318555 2 0.459941742081448 0.3543580459770115 2 0.29679841628959275 0.3919399014778325 2 0.2494505656108597 0.5163434318555008 2 0.39561832579185524 0.49112487684729067 2 0.5089161341926048 0.5317439785097832 2 0.597897850678733 0.5866857963875205 2 0.519897963800905 0.8078048440065682 2

dataset1/valid/labels/1_png.rf.93da46b46fdaa2f4c4ac185c44ca9f99.txt

@@ -0,0 +1 @@
+0 0.5 0.5139559633027523 1 0.9717500917431192 0.2738325221238938 0.30562954128440367 2 0.13247179203539822 0.39494082568807337 2 0.36393926991150444 0.7975252293577981 2 0.5646222345132743 0.1668219266055046 2 0.6244196902654867 0.055982752293577985 2 0.8080345132743363 0.19334660550458715 2 0.8739679203539823 0.1098356880733945 2 0.8826470132743364 0.3109897247706422 2 0.9602350663716814 0.22233366972477064 2 0.9414075221238939 0.5264785321100917 2 0.9813836283185841 0.44794908256880733 2 0.09397721238938053 0.5462657798165138 2 0.49360851769911507 0.28398385321100916 2 0.6951618362831858 0.3346677981651376 2 0.7873921460176991 0.4442783486238532 2 0.8559674778761062 0.612564495412844 2 0.7761757743362833 0.710824862385321 2 0.6729701327433628 0.5734866055045872 2 0.5896289823008849 0.4735522018348624 2 0.4235515486725664 0.4291243119266055 2 0.13619402654867258 0.6343433027522936 2

dataset1/valid/labels/3_png.rf.ead08d1c1581ca5069e20bb9c43fcb3f.txt

@@ -0,0 +1 @@
+0 0.513621408045977 0.4290998905908096 0.7927554597701149 0.7363750547045952 0.16876580459770116 0.4068280087527352 2 0.24165718390804597 0.45862111597374183 2 0.5786484195402299 0.5848263676148796 2 0.4143024425287356 0.18997560175054704 2 0.4021866379310345 0.11170634573304157 2 0.5891155172413793 0.167703829321663 2 0.5850395114942529 0.07755875273522976 2 0.695541235632184 0.2033793216630197 2 0.7041702586206897 0.13231673960612692 2 0.8481360632183907 0.3205667396061269 2 0.8802954022988506 0.2791270240700219 2 0.34893160919540234 0.5336796498905908 2 0.4411859195402299 0.26484179431072213 2 0.5837977011494253 0.25368183807439826 2 0.6782969827586207 0.2876692560175055 2 0.809978591954023 0.3799886214442013 2 0.7925538846317582 0.40554580634941684 2 0.6605968390804597 0.3618410284463895 2 0.5846326149425287 0.3461881838074398 2 0.46500373563218395 0.3763451859956236 2 0.42324727011494256 0.5802265864332604 2

dataset1/valid/labels/7_png.rf.25ad864147ce2ac377b517cba63cc9f3.txt

@@ -0,0 +1 @@
+0 0.4232474820143885 0.5655132653061224 0.663341247002398 0.8295385714285715 0.682094964028777 0.5242718367346938 2 0.6447779376498801 0.6730867346938776 2 0.29968836930455633 0.7527369387755102 2 0.6829255395683453 0.22191224489795916 2 0.725447721822542 0.17950142857142856 2 0.6036607913669065 0.18943979591836735 2 0.6355073141486811 0.1907791836734694 2 0.5298448441247002 0.20130204081632655 2 0.5884667865707434 0.18477367346938778 1 0.40867925659472426 0.20732877551020407 2 0.4567940047961631 0.18752122448979594 2 0.5548841726618705 0.837335306122449 2 0.6288719424460432 0.2849542857142857 2 0.5359400479616306 0.23218 2 0.45934820143884897 0.2504304081632653 2 0.3645364508393285 0.2636426530612245 2 0.3172305755395683 0.3939785714285714 2 0.37513429256594727 0.41553999999999996 2 0.4175846522781775 0.4624255102040817 2 0.518257314148681 0.4946534693877551 2 0.4335406474820144 0.8330728571428571 2

dataset2/train.yaml

@@ -0,0 +1,9 @@
+path: ./dataset1 # 数据集根目录
+train: train/images # 训练集目录名
+val: valid/images # 验证集目录名
+task: pose #任务类型为 pose
+kpt_shape: [7, 3]
+flip_idx: [0, 1, 2, 3, 4, 5, 6]
+
+nc: 1
+names: ['tiger']