Fix dataset numeric values

lab-2/dataset/data.yaml

-train: ./train/images
-val: ./val/images
+train: ./train
+val: ./val
 
 nc: 2
 names: ['kettle', 'teapot']

lab-2/dataset/train/0140.txt

-1 350.8493347167969 354.799072265625 289.423828125 372.03509521484375
+1 0.5482020854949952 0.5543735504150391 0.4522247314453125 0.5813048362731934

lab-2/dataset/train/0141.txt

-1 323.4439392089844 296.4455261230469 503.6419372558594 294.6485595703125
+1 0.5053811550140381 0.4631961345672607 0.7869405269622802 0.4603883743286133

lab-2/dataset/train/0142.txt

-1 335.1315612792969 303.3456726074219 388.26348876953125 419.89862060546875
+1 0.5236430644989014 0.47397761344909667 0.6066617012023926 0.6560915946960449

lab-2/dataset/train/0143.txt

-0 293.34613037109375 335.6320495605469 207.43426513671875 275.1614074707031
+0 0.458353328704834 0.5244250774383545 0.32411603927612304 0.42993969917297364

lab-2/dataset/train/0144.txt

-1 310.807861328125 329.34979248046875 508.07293701171875 599.8754272460938
+1 0.4856372833251953 0.5146090507507324 0.7938639640808105 0.9373053550720215

lab-2/dataset/train/0145.txt

-1 315.0512390136719 329.32086181640625 551.585205078125 365.07025146484375
+1 0.4922675609588623 0.5145638465881348 0.8618518829345703 0.5704222679138183

lab-2/dataset/train/0146.txt

-1 444.50421142578125 279.4408874511719 195.315185546875 205.59255981445312
+1 0.6945378303527832 0.43662638664245607 0.3051799774169922 0.321238374710083

lab-2/dataset/train/0147.txt

-1 421.5524597167969 139.18092346191406 223.48941040039062 276.7925109863281
+1 0.6586757183074952 0.21747019290924072 0.34920220375061034 0.4324882984161377

lab-2/dataset/train/0148.txt

-1 310.8150329589844 318.5168151855469 392.1365051269531 381.3409118652344
+1 0.48564848899841306 0.497682523727417 0.6127132892608642 0.5958451747894287

lab-2/dataset/train/0149.txt

-1 203.342529296875 334.3580322265625 249.88275146484375 298.1532897949219
+1 0.3177227020263672 0.5224344253540039 0.3904417991638184 0.4658645153045654

lab-2/dataset/train/0150.txt

-1 246.48583984375 375.76922607421875 159.29270935058594 218.2156219482422
+1 0.3851341247558594 0.5871394157409668 0.24889485836029052 0.3409619092941284

lab-2/dataset/train/0151.txt

-1 312.6701965332031 334.8675537109375 507.55560302734375 328.8238830566406
+1 0.48854718208312986 0.5232305526733398 0.7930556297302246 0.513787317276001

lab-2/dataset/train/0152.txt

-1 390.2960205078125 259.1832275390625 385.5002136230469 253.814697265625
+1 0.609837532043457 0.40497379302978515 0.6023440837860108 0.39658546447753906

lab-2/dataset/train/0153.txt

-1 319.76727294921875 318.8006896972656 637.18701171875 412.790771484375
+1 0.4996363639831543 0.49812607765197753 0.9956047058105468 0.644985580444336

lab-2/dataset/train/0154.txt

-1 382.9256896972656 311.388671875 202.3446502685547 221.3416748046875
+1 0.5983213901519775 0.4865447998046875 0.3161635160446167 0.34584636688232423

lab-2/dataset/train/0155.txt

-1 302.8484802246094 286.6430358886719 340.7275390625 519.7564086914062
+1 0.47320075035095216 0.4478797435760498 0.5323867797851562 0.8121193885803223

lab-2/dataset/train/0156.txt

-1 314.9270935058594 333.985107421875 607.437255859375 480.2978820800781
+1 0.49207358360290526 0.5218517303466796 0.9491207122802734 0.7504654407501221

lab-2/dataset/train/0157.txt

-1 202.74046325683594 305.8616638183594 195.1556396484375 359.7783203125
+1 0.31678197383880613 0.4779088497161865 0.3049306869506836 0.5621536254882813

lab-2/dataset/train/0158.txt

-1 317.0753173828125 319.970458984375 609.4284057617188 458.7991027832031
+1 0.49543018341064454 0.4999538421630859 0.9522318840026855 0.7168735980987548