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dataset1/README.dataset.txt

@@ -1,6 +0,0 @@
-# Hand Pose Estimation > 2025-09-27 7:55pm
-https://universe.roboflow.com/crabapples/hand-pose-estimation-hfn9o-kvebv
-
-Provided by a Roboflow user
-License: CC BY 4.0
-

dataset1/README.roboflow.txt

@@ -1,27 +0,0 @@
-
-Hand Pose Estimation - v1 2025-09-27 7:55pm
-==============================
-
-This dataset was exported via roboflow.com on September 27, 2025 at 11:55 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 70 images.
-Hand 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: [21, 3]
-flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
-
-nc: 1
-names: ['hand']
-
-roboflow:
-  workspace: crabapples
-  project: hand-pose-estimation-hfn9o-kvebv
-  version: 1
-  license: CC BY 4.0
-  url: https://universe.roboflow.com/crabapples/hand-pose-estimation-hfn9o-kvebv/dataset/1

dataset1/train.yaml

@@ -7,10 +7,11 @@ task: pose #任务类型为 pose
 loss: CIou
 
 
-#nc: 2 # 类别数
-#names: ['left_hand', 'right_hand']
+nc: 2 # 类别数
+names: ['left_hand', 'right_hand']
+
+#nc: 1
+#names: ['hand']
 
-nc: 1
-names: ['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)
 flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] # 关键点

dataset1/train/labels/11_png.rf.6c58512c6f7badc47521504deb10a78c.txt

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dataset1/train/labels/13_png.rf.6cf1c8e9887924afb2f0390eb9073c98.txt

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dataset1/train/labels/14_png.rf.e7a0ef273d11f70eb78cfcf68adeb918.txt

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dataset1/train/labels/15_png.rf.8e317459ea8ec0b5cdf7313d1f104102.txt

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dataset1/train/labels/16_png.rf.73d63a0fa202f7ba858a8485cac9a50f.txt

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dataset1/train/labels/17_png.rf.de75c5581a7f56597efd0aad51cc4ae0.txt

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dataset1/train/labels/2_png.rf.a45c0e16dd47cf0a87e9465f3d325d40.txt

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dataset1/train/labels/4_png.rf.3d4899d6b2a9ecc28051a7945906ea3b.txt

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dataset1/train/labels/5_png.rf.143f25c8f0cb4f08678116b68d8f270a.txt

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dataset1/train/labels/6_png.rf.6877adcd55b42c9ff010216580151895.txt

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dataset1/train/labels/8_png.rf.b69e27a94f339eae40b005092f026b1a.txt

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dataset1/train/labels/9_png.rf.a382e10a8ab000be7d7d5b3772285b67.txt

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dataset1/valid/labels/10_png.rf.4b4b9f3ca0524e72176c9ada75cb6e60.txt

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dataset1/valid/labels/12_png.rf.6dada87a406c5580bd3d1973cdfc2f62.txt

@@ -0,0 +1 @@
+0 0.4936537330316742 0.5425261904761906 0.9205832579185521 0.9149477011494254 0.8759088235294118 0.6103114942528736 2 0.7841516968325792 0.6851117405582923 2 0.2937199095022624 0.7704527914614122 2 0.755952149321267 0.30534285714285714 2 0.8011863122171946 0.23459950738916258 2 0.6568597285067873 0.22007668308702794 2 0.690214592760181 0.1228159277504105 2 0.5206565610859728 0.23525164203612478 2 0.5551307692307692 0.15799786535303775 2 0.32852556561085977 0.3079405582922824 2 0.34063325791855203 0.2312694581280788 2 0.6403921945701359 0.7991616584564861 2 0.7109904977375565 0.4065536124794745 2 0.5977799773755657 0.3528559934318555 2 0.4601773755656109 0.3543580459770115 2 0.29703404977375564 0.3919399014778325 2 0.24968619909502263 0.5163434318555008 2 0.39585395927601813 0.49112487684729067 2 0.5091517676767677 0.5317439785097832 2 0.5981334841628959 0.5866857963875205 2 0.5201335972850678 0.8078048440065682 2

dataset1/valid/labels/1_png.rf.3c179d5056929ac49f879f0b300f81b5.txt

@@ -0,0 +1 @@
+0 0.5 0.5141249541284404 1 0.9717500917431192 0.2738325221238938 0.3057985321100917 2 0.13247179203539822 0.3951098165137614 2 0.36393926991150444 0.7976942201834862 2 0.5646222345132743 0.16699091743119268 2 0.6244196902654867 0.05615174311926605 2 0.8080345132743363 0.1935155963302752 2 0.8739679203539823 0.11000467889908257 2 0.8826470132743364 0.3111587155963303 2 0.9602350663716814 0.2225026605504587 2 0.9414075221238939 0.5266475229357798 2 0.9813836283185841 0.4481180733944954 2 0.09397721238938053 0.5464347706422018 2 0.49360851769911507 0.28415284403669727 2 0.6951618362831858 0.3348367889908257 2 0.7873921460176991 0.4444473394495413 2 0.8559674778761062 0.6127334862385321 2 0.7761757743362833 0.7109938532110092 2 0.6729701327433628 0.5736555963302752 2 0.5896289823008849 0.4737211926605504 2 0.42555774336283186 0.43453577981651376 2 0.13619402654867258 0.6345122935779816 2

dataset1/valid/labels/3_png.rf.9654f9ee2f83f0f72ded72926d2ce61a.txt

@@ -0,0 +1 @@
+0 0.5138458333333333 0.42927078774617067 0.7927554597701149 0.7363750547045952 0.16899022988505746 0.4069989059080963 2 0.2418816091954023 0.45879201312910284 2 0.5788728448275863 0.5849972647702407 2 0.41452686781609194 0.1901464989059081 2 0.4024110632183908 0.11187724288840262 2 0.5893399425287357 0.16787472647702406 2 0.5852639367816093 0.07772964989059081 2 0.6957656609195403 0.20355021881838076 2 0.7043946839080459 0.13248763676148798 2 0.8483604885057471 0.320737636761488 2 0.880519827586207 0.27929792122538294 2 0.3491560344827586 0.5338505470459518 2 0.44141034482758623 0.26501269146608314 2 0.5840221264367816 0.2538527352297593 2 0.678521408045977 0.2878401531728665 2 0.8102030172413793 0.3801595185995624 2 0.7927783099191146 0.4057167035047779 2 0.6608212643678161 0.36201192560175055 2 0.584857040229885 0.3463590809628009 2 0.4652281609195403 0.37651608315098467 2 0.4234716954022989 0.5803974835886214 2

dataset1/valid/labels/7_png.rf.f074684ae100774f8365628325b4f2d3.txt

@@ -0,0 +1 @@
+0 0.42334736211031176 0.5655132653061224 0.663341247002398 0.8295385714285715 0.6821948441247003 0.5242718367346938 2 0.6448778177458033 0.6730867346938776 2 0.2997882494004796 0.7527369387755102 2 0.6830254196642686 0.22191224489795916 2 0.7255476019184652 0.17950142857142856 2 0.6037606714628297 0.18943979591836735 2 0.6356071942446043 0.1907791836734694 2 0.5299447242206236 0.20130204081632655 2 0.5885666666666667 0.18477367346938778 1 0.4087791366906475 0.20732877551020407 2 0.4568938848920864 0.18752122448979594 2 0.5549840527577937 0.837335306122449 2 0.6289718225419664 0.2849542857142857 2 0.536039928057554 0.23218 2 0.45944808153477223 0.2504304081632653 2 0.3646363309352518 0.2636426530612245 2 0.31733045563549156 0.3939785714285714 2 0.3752341726618705 0.41553999999999996 2 0.41768453237410075 0.4624255102040817 2 0.5183571942446044 0.4946534693877551 2 0.43364052757793764 0.8330728571428571 2

dataset3/train.yaml

@@ -0,0 +1,16 @@
+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
+test: test/images
+
+task: pose #任务类型为 pose
+loss: CIou
+
+
+#nc: 2 # 类别数
+#names: ['left_hand', 'right_hand']
+
+nc: 1
+names: ['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)
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] # 关键点