Kvasir-SEG_Yolo-SAM_2/notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPBwweBrB4o8uIDXfV5tPCf"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OVV7S13IWcVB","executionInfo":{"status":"ok","timestamp":1754416319029,"user_tz":-345,"elapsed":18994,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"3a826183-7ec2-486a-f9ae-34ca962d0ada"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'YOLO_SAM2'...\n","remote: Enumerating objects: 76, done.\u001b[K\n","remote: Counting objects: 100% (10/10), done.\u001b[K\n","remote: Compressing objects: 100% (10/10), done.\u001b[K\n","remote: Total 76 (delta 4), reused 0 (delta 0), pack-reused 66 (from 3)\u001b[K\n","Receiving objects: 100% (76/76), 234.75 MiB | 14.97 MiB/s, done.\n","Resolving deltas: 100% (10/10), done.\n","Updating files: 100% (48/48), done.\n"]}],"source":["!git clone https://github.com/sajjad-sh33/YOLO_SAM2.git"]},{"cell_type":"code","source":["import kagglehub\n","import zipfile\n","import shutil\n","import os\n","\n","# Step 1: Download the dataset using kagglehub\n","dataset_path = kagglehub.dataset_download(\"debeshjha1/kvasirseg\")\n","\n","# Step 2: Create a target path in /content\n","target_dir = \"/content/kvasirseg\"\n","os.makedirs(target_dir, exist_ok=True)\n","\n","# Step 3: Find and unzip any zip files from the download path\n","for root, dirs, files in os.walk(dataset_path):\n","    for file in files:\n","        if file.endswith(\".zip\"):\n","            zip_path = os.path.join(root, file)\n","            with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","                zip_ref.extractall(target_dir)\n","\n","# Step 4: (Optional) Copy non-zip files to /content too\n","for root, dirs, files in os.walk(dataset_path):\n","    for file in files:\n","        if not file.endswith(\".zip\"):\n","            shutil.copy(os.path.join(root, file), target_dir)\n","\n","print(\"✅ Dataset unzipped at:\", target_dir)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ogThMtFIXOXf","executionInfo":{"status":"ok","timestamp":1754416363992,"user_tz":-345,"elapsed":18109,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"20dc8c8c-ea00-44f8-9d56-220924ad6f9c"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading from https://www.kaggle.com/api/v1/datasets/download/debeshjha1/kvasirseg?dataset_version_number=3...\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 144M/144M [00:07<00:00, 20.1MB/s]"]},{"output_type":"stream","name":"stdout","text":["Extracting files...\n"]},{"output_type":"stream","name":"stderr","text":["\n"]},{"output_type":"stream","name":"stdout","text":["✅ Dataset unzipped at: /content/kvasirseg\n"]}]},{"cell_type":"code","source":["# Define the path to train.txt inside folder 'a'\n","file_path = '/content/kvasirseg/train.txt'\n","\n","# Open and print contents\n","with open(file_path, 'r') as f:\n","    for line in f:\n","        print(line.strip())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"t42l2lJoc_cZ","executionInfo":{"status":"ok","timestamp":1754417910655,"user_tz":-345,"elapsed":67,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"c6d6ca5c-435d-43de-c5d7-e8bec05cd1c8"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["cju0qkwl35piu0993l0dewei2\n","cju0qoxqj9q6s0835b43399p4\n","cju0qx73cjw570799j4n5cjze\n","cju0roawvklrq0799vmjorwfv\n","cju0rx1idathl0835detmsp84\n","cju0s2a9ekvms080138tjjpxr\n","cju171py4qiha0835u8sl59ds\n","cju175facms5f0993a5tjikvt\n","cju17bz250pgd0799u1hqkj5u\n","cju17g6ykn1cs0993dww6qdi8\n","cju17hw9hr9c5098800fu4u8e\n","cju17otoe119u0799nqcbl8n1\n","cju17r8il13910799dr2wme2e\n","cju17v6ih0u7808783zcbg1jy\n","cju17x0j4nfc10993y31pvlgs\n","cju17z0qongpa0993de4boim4\n","cju1819curo000988pd5xcqme\n","cju183od81ff608017ekzif89\n","cju1871y11d6r0799k6cw4yze\n","cju18849rrsgr0988p90hkygb\n","cju18gzrq18zw0878wbf4ftw6\n","cju18ibp219ub08783i6o98g7\n","cju18kevfrojc0835bn90f1in\n","cju1alwgo30z60855fm3y23sm\n","cju1amqw6p8pw0993d9gc5crl\n","cju1aqqv02qwz0878a5cyhr67\n","cju1ats0y372e08011yazcsxm\n","cju1b0y2e396p08558ois175d\n","cju1b3zgj3d8e0801kpolea6c\n","cju1b75x63ddl0799sdp0i2j3\n","cju1bhnfitmge0835ynls0l6b\n","cju1bm8063nmh07996rsjjemq\n","cju1brhsj3rls0855a1vgdlen\n","cju1c0qb4tzi308355wtsnp0y\n","cju1c3218411b08014g9f6gig\n","cju1c4fcu40hl07992b8gj0c8\n","cju1c6yfz42md08550zgoz3pw\n","cju1c8ffau5770835g0g343o8\n","cju1cbokpuiw70988j4lq1fpi\n","cju1cdxvz48hw0801i0fjwcnk\n","cju1cfhyg48bb0799cl5pr2jh\n","cju1cj3f0qi5n0993ut8f49rj\n","cju1cnnziug1l0835yh4ropyg\n","cju1cqc7n4gpy0855jt246k68\n","cju1csmlc4ht10799b8ymmghg\n","cju1cu1u2474n0878tt7v4tdr\n","cju1cvkfwqrec0993wbp1jlzm\n","cju1cyjb5qtie0993njqne9m3\n","cju1d31sp4d4k0878r3fr02ul\n","cju1d50a94qf50855wsowacrc\n","cju1d96gsv62d09881b3wecw2\n","cju1ddr6p4k5z08780uuuzit2\n","cju1dfeupuzlw0835gnxip369\n","cju1dg44i4z3w0801nyz4p6zf\n","cju1dhfok4mhe0878jlgrag0h\n","cju1dia8hvc6v098827mgffnm\n","cju1djtprvd7b0988thwwrg09\n","cju1dnz61vfp40988e78bkjga\n","cju1dq3x1vgx109889c7wyirg\n","cju1drnhbrb9409935wi7vkhg\n","cju1efbr0rqxz09931z0lf4vf\n","cju1egh885m1l0855ci1lt37c\n","cju1egx9pvz2n0988eoy8jp23\n","cju1ejj7dvqfa0835ra184v5m\n","cju1erep75us208553i4ofwwe\n","cju1euant5l960878iqj5vvto\n","cju1euuc65wm00799m4sjdnnn\n","cju1ewnoh5z030855vpex9uzt\n","cju1expq45zst0855rjqwwj4m\n","cju1f15k3w4ct0835cmde6ypo\n","cju1f320ewfyu0988ndz6blh5\n","cju1f5x1164xv08555654c24r\n","cju1f79yhsb5w0993txub59ol\n","cju1f8w0t65en0799m9oacq0q\n","cju1fb9236a110801yvg0fwju\n","cju1ffnjn6ctm08015perkg37\n","cju1fj6axwfp30835ukhuzhw9\n","cju1fjsb4sipq09931lvd8e41\n","cju1fm3id6gl50801r3fok20c\n","cju1fmsyf6gxb0801cimx2gle\n","cju1fr4etsmrr09933u4t4aql\n","cju1ftaji6isw0855108yqcse\n","cju1fuoa4wmc50835qfd11sp9\n","cju1fyb1d69et0878muzdak9u\n","cju1g20bdwq6u0835e16xugcd\n","cju1g4nsb6ngy0799l4ezm8ab\n","cju1gghyjwxt80835vx0wgxw0\n","cju1gi7jlwyld0835cdf6g6qz\n","cju1gkndf6yi10801o1qnje19\n","cju1gv7106qd008784gk603mg\n","cju1h5w4wxajx0835mc954kxy\n","cju1h89h6xbnx08352k2790o9\n","cju1haab178i70799tk9z8y8x\n","cju1hhj6mxfp90835n3wofrap\n","cju1hirfi7ekp0855q0vgm9qq\n","cju1hmff8tkp809931jps6fbr\n","cju1hp9i2xu8e0988u2dazk7m\n","cju1hs0za7jha0855vj0mdrjt\n","cju1hyolc7aqu0878rrkfn1lr\n","cju2hdr06v2bq0799mbm3bks1\n","cju2hewssldzx0835ep795xu0\n","cju2hfqnmhisa0993gpleeldd\n","cju2hgsptlfam0835o3b59h1o\n","cju2hjrqcvi2j0801bx1i6gxg\n","cju2hlm19vjjf0801o69qnber\n","cju2hos57llxm08359g92p6jj\n","cju2hqt33lmra0988fr5ijv8j\n","cju2htabevq9108015qjei0x7\n","cju2hugv9vget0799hhk7ksvg\n","cju2hw5gjlr5h0988so2qqres\n","cju2hx006vidl0799igm81vmh\n","cju2i03ptvkiu0799xbbd4det\n","cju2i3hzclw3o0988rrgh911i\n","cju2i6acqvo6l0799u20fift8\n","cju2i8br1vqtd08784u6vmcjk\n","cju2iatlki5u309930zmgkv6h\n","cju2igw4gvxds0878808qj398\n","cju2ij9uiic2l09933ljiv6gm\n","cju2lberzkdzm09938cl40pog\n","cju2lcyfgkf5809932fn9gucn\n","cju2lejzcy4pc0878c9rlonot\n","cju2lyynuymli0855g7fxgbhe\n","cju2lz8vqktne0993fuym6drw\n","cju2m56cryvqd0801gtn2yp8t\n","cju2m71z2ywwv080131bcrsd3\n","cju2ma647l0nj0993ot4deq2q\n","cju2mfjndoz700988b9lc3zeq\n","cju2mh8t6p07008350e01tx2a\n","cju2nbdpmlmcj0993s1cht0dz\n","cju2nd7l7z98o0799gfjvyfmw\n","cju2nfnvxzdkd0878399axlco\n","cju2nguelpmlj0835rojdn097\n","cju2nnqrqzp580855z8mhzgd6\n","cju2np2k9zi3v079992ypxqkn\n","cju2nqapmzvk20801f9us40dx\n","cju2nsmwjlzyl0993jl80chvz\n","cju2ntxtdzlvu0799xl3j9pan\n","cju2nyc5f02m40801ojqbtiea\n","cju2oi8sq0i2y0801mektzvw8\n","cju2okvco06xc0799kxe5n1qh\n","cju2omjpeqj5a0988pjdlb8l1\n","cju2oo0wh0bqy0878biujeyhe\n","cju2oq5570avm079959o20op1\n","cju2osuru0ki00855txo0n3uu\n","cju2otvvv0l7z0855x7we8cb0\n","cju2ouil2mssu0993hvxsed6d\n","cju2p0eveqtdc0835gpi3p93i\n","cju2p4ddkmzxj0993p94o62av\n","cju2p91qir00k08350ddfif0w\n","cju2pag1f0s4r0878h52uq83s\n","cju2phaksnahz0993yxogjcpv\n","cju2pjb9v0ywn0878j5g5n69j\n","cju2pkwt3r8b90988v2ywq1px\n","cju2pmhtr17a00855cvpelzb0\n","cju2qdj95ru8g09886gfi9rsz\n","cju2qfie4rvz508357kad9z5o\n","cju2qh5le1ock0878oahaql7d\n","cju2qn2fzs1vy0988l243cvzy\n","cju2qozsk20cq0855ugrg3cri\n","cju2qqn5ys4uo0988ewrt2ip2\n","cju2qs32r1vys07999conmbvx\n","cju2qtee81yd708787bsjr75d\n","cju2qu37qobl50993aw7ghcfq\n","cju2qvuj1s9ok0835tp2k4ozh\n","cju2qxxko229x08786gvxxhur\n","cju2qz06823a40878ojcz9ccx\n","cju2r11x7sdgx0988o8ule0wl\n","cju2r2obh2bjm08553kng0rh7\n","cju2r6mt2om21099352pny5gw\n","cju2r7h21sj9608354gzks3ae\n","cju2r91dg2k090801bh0xzbxk\n","cju2raxlosl630988jdbfy9b0\n","cju2rga4psq9n09881z519xx0\n","cju2ricdv2iys0878sv1adh0u\n","cju2rkjfwoxys0993x768l1j2\n","cju2rlqdnoz9k0993cpjae3x0\n","cju2rmd2rsw9g09888hh1efu0\n","cju2rn0hasxri0835nfy3buay\n","cju2rnkt22xep0801as160g9t\n","cju2ro5jqsy680988pi6qsujw\n","cju2rpa30t07b0835im0erql0\n","cju2rqo702wpx0855fn7d5cxh\n","cju2rxm8rpbaf0993o3qr2oph\n","cju2rz4k434s70855wwx3ddtx\n","cju2rzpsmtb0f0835jabkbao1\n","cju2s16zp317h0799gr67jqc2\n","cju2s2527pfyr0993l3h1149a\n","cju2s9g11pnra0993gn4eh793\n","cju2saez63gxl08559ucjq3kt\n","cju2sevf53lkx08558h5bpaig\n","cju2sggy13na70855tbeoqgha\n","cju2spdagu1l50835da1f46fr\n","cju2srvy5440s0801y1ba9akr\n","cju2sszfq3uye0878sucelzk2\n","cju2suk42469908015ngmq6f2\n","cju2sxf3iqbpv09937iksn8ep\n","cju2syxa93yw40799x2iuwabz\n","cju2t16vuucaq0835xcpsivn2\n","cju2t2ivz43i10878zeg8r1br\n","cju2t3ibkuecr0835o7si16zv\n","cju2t62nq45jl0799odpufwx6\n","cju2t9tdwuk700835kv0ljmtl\n","cju2ti1du4idn0878giuozonw\n","cju2tjrog4jy30878pawyazqc\n","cju2top2ruxxy0988p1svx36g\n","cju2tpfa5uyx408359datxqqj\n","cju2tqfgw4oat0799rn0g5b2z\n","cju2trbpkv0c00988hxla5dzz\n","cju2trtjf4qjd0878a2zle9v9\n","cju2tvrvm53ws0801a0jfjdxg\n","cju2txjfzv60w098839dcimys\n","cju2tzypl4wss0799ow05oxb9\n","cju2u2b9o4zvp08788qb9nqxj\n","cju2u4pymvc720988wsxrmi84\n","cju2u73dj53oz0878486k8k4b\n","cju2ueb6j5ado0878vf5md13o\n","cju2ulk385h170799rlklxob0\n","cju2uokeg5jm20799xwgsyz89\n","cju2upu4evw7g08358guwozxv\n","cju2urqpwvxw70835rvndvtsi\n","cju2uwz9f5yf1085506cfamfx\n","cju2uy8ox62jo0801g88hh42z\n","cju2uzabhs6er0993x3aaf87p\n","cju2wtwj87kys0855kx6mddzw\n","cju2wve9v7esz0878mxsdcy04\n","cju2wx0gh7fpz0878wwyd9ep8\n","cju2wxv0hxs2f09884w48v8fi\n","cju2wzu8wxtgu09880ku9x1pg\n","cju2x7vw87mu30878hye2ca0m\n","cju2xa3i4y0160988i679zsqd\n","cju2xbk0080y80801eghyddi2\n","cju2xd75m82720801q4s4ik3n\n","cju2xf8e5y2wm08359vcgk09b\n","cju2xjz2ju8pe0993ysv9wg17\n","cju2xlcqxy9c60988vjacdznb\n","cju2xs6na81t20878pt6nkfip\n","cju2xwm1s84l50799i60mq0pu\n","cju2xyd9vyi7m098831qcucse\n","cju2y0z6g87p10878fpk5d3rq\n","cju2y26c588bo07993ksd8eoz\n","cju2y40d8ulqo0993q0adtgtb\n","cju2y5zas8m7f0801d34g5owq\n","cju2y8s56ymqr083541ggdsml\n","cju2yb31a8e8u0878wdashg7o\n","cju2ycp1u8g2r0799jslnp7cz\n","cju2yg5ht8i4p087800js8hp4\n","cju2yi9tz8vky0801yqip0xyl\n","cju2yljr0yzhw0988ecf271ly\n","cju2yo1j1v0qz09934o0e683p\n","cju2ysg748ru80878sp6j0gm0\n","cju2yv4imv6cz099314jveiib\n","cju2yw4s7z7p20988lmf2gdgd\n","cju2yyhsp933j0855hp32e012\n","cju2z1nxlzaj40835wj81s1iy\n","cju2z2x3nvd3c099350zgty7w\n","cju2z45kuzf6d0988nz2c819m\n","cju2z6ez69g4u0801qwt088lw\n","cju2z9vlp9j0w0801oag91sy9\n","cju2zblxw9848087853csbrx1\n","cju2zdhsczmn50988z64qwg2q\n","cju2zdvjn9h7r08553cp4eed5\n","cju2zgbj9zmrw0835nnlzxj4c\n","cju2zi4l09f5807991s8do2b2\n","cju2zjcvj9qma0801dk71hhi0\n","cju2zkpdl9h7t0799ix60teqg\n","cju2zm0axztpe0988r8s9twjr\n","cju2zo0fwzv580988qlijd2xa\n","cju2zp89k9q1g0855k1x0f1xa\n","cju2zpw4q9vzr0801p0lysjdl\n","cju2zr3c3vwb00993jn06bbaz\n","cju2zrojo9kcd0878ld2epejq\n","cju2zwg05a0oy0801yr73ig7g\n","cju2zxja9w1eh09933609ho9z\n","cju2zy1e49pqk0878t6ncqn12\n","cju300m3s04fg0988uzupuf7z\n","cju302fqq9spc0878rrygyzzz\n","cju303j5r062k098835zxfds5\n","cju30525w04r10835ygp257sb\n","cju306x7w05nb0835cunv799x\n","cju30ajhw09sx0988qyahx9s8\n","cju30bmab08bi0835mvlr6e0r\n","cju30df2j09dd08351ayx2t6w\n","cju30ftgja7170855xl9bkdm0\n","cju30gxjq0djk0988jytm49rs\n","cju30ia8da2bq0799klnehml2\n","cju30j1rgadut0801vuyrsnt8\n","cju30k2z40ds308353kdew70n\n","cju30lncba3ny0878jwnous8n\n","cju30mm25a53s0799qa5wiqe8\n","cju30nyxe0gfb0835p256yoju\n","cju30ov1oah920801mi8thuyg\n","cju30qbm1ad3x0855znuhpz9u\n","cju30u1hbakn808019g15nb8b\n","cju30xqmh0ni00835ix3batv1\n","cju30ywtc0oar0835bp2en7ec\n","cju310f6val1v0855xo8tc3gu\n","cju3128yi0rpu0988o4oo5n8n\n","cju31rb7vb6110801p9rhacuw\n","cju31t8xd17bk0835rnb893jk\n","cju31ugmfb3dz0855xtqshki6\n","cju31w6goazci0799n014ly1q\n","cju31y80qbawn0801twwm2l5s\n","cju320gyvbch60801v2amdi2g\n","cju323ypb1fbb0988gx5rzudb\n","cju324q101fhe08350wae9cif\n","cju326h4v1gxw08352px40p7r\n","cju3280wv1ir009882jze27tc\n","cju32a52lb9rc0799xi40qs00\n","cju32csyfblyh080170aa3x5p\n","cju32fhnhbds40799broyoptc\n","cju32gzs6xo8x0993r8tedbpb\n","cju32jcdabepz0878d0cznmfe\n","cju32l161bi1v07990vm376in\n","cju32phw2bv130801yj7bkouq\n","cju32pzh9bpw10855q4vaxfhe\n","cju33yemn2qb20988wfjxximx\n","cju33za6l2qy70988jhrlp2ev\n","cju34aozyyy830993bn16u32n\n","cju34c1xfyz920993itxkkfad\n","cju34ds2531520988qjpqt6e3\n","cju34eqjpcpm508788b3lhp97\n","cju34fojcctcf0799ebolbvkn\n","cju34i3qvcyog0855qiejxx5w\n","cju34m7h536wq0988xz7gx79v\n","cju34o6dbd2lo0855aqlcy1hs\n","cju34ouumcznz07996gg1xq7v\n","cju34repocy5208780gswillm\n","cju34sh43d8zm08019xbwhc0o\n","cju34uhepd3dd0799hs8782ad\n","cju34xspwzenf0993cyzajv9n\n","cju34ymm8d6700799uop0cw33\n","cju34zivp3fq80988opxbaqyn\n","cju3518w2d838079939fqztbc\n","cju3521y5d5mq0878t3ezsu4p\n","cju353d1eda8c07992afde611\n","cju35740hzm0g0993zl5ic246\n","cju357rxxdaz30878y2esjpjt\n","cju358pwtdby20878cg7nm0np\n","cju35a77vdj4n08556jj2lgmc\n","cju35atpxdjot0855q46aqrd0\n","cju35c4wzdhow0799h6eq4sgs\n","cju35eg0tdmjt085525sb4bua\n","cju35fxqyzt5p0993vusm54qz\n","cju35i2e63uxr0835h7zgkg9k\n","cju35k2fr3vc50988c85qkrwg\n","cju35ldepdtlm0801yv79y8vu\n","cju35mdz73x890835eynq1h9v\n","cju35oyvd3y850988km12hdz1\n","cju3tp94kfstl08181awh6z49\n","cju3tsh4lfsok0987w6x3a0v1\n","cju3ttznuftyf09875t11850w\n","cju3tvffffx5f0818t5ov22al\n","cju3tx1qyg0c907552fglumhc\n","cju3u1c8tfyqx08503iedc3mx\n","cju3u39fog1bo0871lxjrabks\n","cju3u4lxmg59o0755rz42b9en\n","cju3u815rg4ek0850vvhtcvcm\n","cju3ua8u0g9rg0801uayhdxhu\n","cju3uhb79gcgr0871orbrbi3x\n","cju3ul8dogf1z09872y2ecowp\n","cju3umoh1geet0817cmpef5am\n","cju3uwz6ogsp10801h2r3bj5l\n","cju3uz4o6gr9z0850lhxyxvsj\n","cju3v0fl3gwce0755qkjhzmd4\n","cju3v11mrgwwb0755u242ygye\n","cju3v3ac9gyz30755hfqwyp1i\n","cju3v56bwgy8v0871w14pz8fx\n","cju3v664kh0px0818y4y7wolf\n","cju3v72v5h1qz0818fggilwtq\n","cju3wstckialg0871xs0vevsj\n","cju3x2s11ibzi0817kk284k0j\n","cju3x4blzieu30850x10uuvbm\n","cju3x5u2tiihx0818914gzxy1\n","cju3x7xsaijq80818f0psavav\n","cju3x9lttikfb0818a0g104zn\n","cju3xeexgii1j0817zs68tb4g\n","cju3xga12iixg0817dijbvjxw\n","cju3xhpvvimda0987ygrpzni2\n","cju3xiic0ilzp0850lusrb42j\n","cju3xjqtpikx50817tppy6g84\n","cju3xl264ingx0850rcf0rshj\n","cju3xtufwiv9c0818djsc4cqd\n","cju3xuj20ivgp0818mij8bjrd\n","cju3xvoo2iqlc0817eku2r3wl\n","cju3xwpgviwlx0871rwm15q7v\n","cju3xzvnzj0hd0755xprz39nj\n","cju3y0pjrj1c30755nxekxccj\n","cju3y21quj0ir0818kgjagr15\n","cju3y54kwj3nr0801biidlb4e\n","cju3y79ofj3va0871uqfb1mzo\n","cju3y9difj6th0801kd1rqm3w\n","cju3ya7goj6at0818v2l5ay7f\n","cju3yb47cj1xq0817zfotbni4\n","cju3yht87j83m08507yk1u1fg\n","cju3ykamdj9u208503pygyuc8\n","cju40jl7skiuo0817p0smlgg8\n","cju40m0rjkpw80871z6n6yg1u\n","cju40poe4kt9s0755f9cnm3h5\n","cju40r6jrksyk0871wg98zgho\n","cju40sdwukv3k0755y99ug1k8\n","cju40taxlkrho0987smigg0x0\n","cju40u30gkuzc0871rq7t666d\n","cju40w3hbkwpn08015rbs3wko\n","cju40wto5kxwi0755it190f2k\n","cju410dnfl0960755y8lu8d79\n","cju412uwlkva50850d1ps1ww7\n","cju414lf2l1lt0801rl3hjllj\n","cju418rckl3ur08012psrx1r1\n","cju41kd7yl4nm0850gil5qqwh\n","cju41lojblbs307555jdci937\n","cju41nz76lcxu0755cya2qefx\n","cju41p90plcsx08018cnzpndc\n","cju41r6v2lcww0871ps8k8pf5\n","cju41s6nbleqy0755e2mslg0b\n","cju41z76wlgbz0801qdetlvby\n","cju422cm8lfxn0818ojicxejb\n","cju424hy5lckr085073fva1ok\n","cju426tomlhll0818fc0i7nvh\n","cju428k5fldt108177s6g6f45\n","cju42dwedlmk60871jbgu4ehi\n","cju42g865lorv07552ytz6xxa\n","cju42m60jlpcm08186kqppzqv\n","cju42nm68lpyo0818xvvqmupq\n","cju42py9mlqyd0818u3d1d7ga\n","cju42qet0lsq90871e50xbnuv\n","cju42romflni20817etb9a0fl\n","cju42tauqlo5p08171l3cuo4b\n","cju42u5bjlvi10801dc13sskp\n","cju42wamblrqn098798r2yyok\n","cju42xpi8lw4w0871ve317a1p\n","cju430pm2lz0y0755jkhcc3d1\n","cju439oazm2fu0871ma0vvrft\n","cju43b8daly4408170e5ev06g\n","cju43c92lm5cj0755lorsorfg\n","cju43eigtm6ev0801mv0m96t1\n","cju43gfosm63n08714rpih8pe\n","cju43h43am1dy08176gwfhmnt\n","cju43in5fm22c08175rxziqrk\n","cju43jcqim2cp08172dvjvyui\n","cju43kj2pm34f0850l28ahpni\n","cju43lcnum9y10755bjs7z87f\n","cju43mkj9m8wb0871qiadahub\n","cju43o6n7m9nk087191ijwqq9\n","cju45jpvfn6c809873pv1i34s\n","cju45lbgznahl08180xz1h7u6\n","cju45n0oxn5vu08500yfrt9jn\n","cju45ofd9ne1j0801ri8dup7t\n","cju45pm27n80u08174kyow1gj\n","cju45qbf3n9sa0987oonbkly9\n","cju45rj7ln8980850a7821fov\n","cju45t5ddnbio0987qtqzx762\n","cju45ty6zn9oz0850qy4qnck1\n","cju45v0pungu40871acnwtmu5\n","cju5b9oyda4yr0850g9viziyv\n","cju5bbtwsa8cl0987wgfsqpao\n","cju5bdwa3aatx0818b79i18zf\n","cju5bf6hxa6m50817rbwettgu\n","cju5bhv81abur0850ean02atv\n","cju5bj926aiec07559rshy4wa\n","cju5bmhdcafs909878qfzrqzi\n","cju5boicjagt20871b1fotkh4\n","cju5buy2bal250818ipl6fqwv\n","cju5bwhapakm90987c1v4z46a\n","cju5bycdkalkb09875f7bfrvx\n","cju5c5xc7algd0817pb1ej5yo\n","cju5c7oijaqmq09878qwgqv8n\n","cju5ca9hcatkc0801jzwe7tfx\n","cju5ccpvqash50850kb4bs22k\n","cju5cetivauok0987ok3e5bre\n","cju5chrxxawka0871qcj171yz\n","cju5cjh3xattc0817j2vbulzi\n","cju5cky5xb0ay0801oxet697t\n","cju5clr68b48r0755cmuvponm\n","cju5cu8qkb84x08186jwo8yin\n","cju5ddda9bkkt0850enzwatb1\n","cju5eftctcdbj08712gdp989f\n","cju5ekty5ckzf07550c9u3ckk\n","cju5enq1tcn1i0755hnkon787\n","cju5eq8c8ck690850vix98hv3\n","cju5es375cnzy0801nkq35ffs\n","cju5ew4h9cqaf0818rrczkmqh\n","cju5eyfe9cpk90987laa7tsl3\n","cju5f0dezct4q08183ydw11dx\n","cju5f26ebcuai0818xlwh6116\n","cju5f8hxdcxxn08188obby0ea\n","cju5fb86jd1jp0755b1ukbhq5\n","cju5fi0yxd3ei0801v7u0yudn\n","cju5fs6j6d8350801vglraq4u\n","cju5ft6mcd5q40987rhjgbrr6\n","cju5fu081d8gc0818l3yylujk\n","cju5fw37edaae0801vkwvocn7\n","cju5fydrud94708507vo6oy21\n","cju5g163vd6mt0817uccuga6u\n","cju5gucasds9d0801019axylx\n","cju5h57xedz5h0755mjpc8694\n","cju5hi52odyf90817prvcwg45\n","cju5hjxaae3i40850h5z2laf5\n","cju5hl8nee8a40755fm8qjj0o\n","cju5hqz50e7o90850e0prlpa0\n","cju5ht88gedbu0755xrcuddcx\n","cju5huurrecm70801y680y13m\n","cju5hwonqedw10801vsd3w6kk\n","cju5hyi9yegob0755ho3do8en\n","cju5i39mreass0817au8p22zy\n","cju5i5oh2efg60987ez6cpf72\n","cju5jx7jzf7c90871c2i9aiov\n","cju5jz5fff8c50871hbe6108f\n","cju5k3j3uf6de0817hszzfr7n\n","cju5k503sfa5f0871lx0rpu5y\n","cju5k7r0yf98c09878csbxb4d\n","cju5klveuff6w0871wbibgh3m\n","cju5knbbqfipk080128cggukq\n","cju5kre09fhka0850h7b1898j\n","cju5ktjwofed70817eg58ef7u\n","cju5nxkujgscq0817l9gss626\n","cju5nyu31gv8e0871zpk74a2n\n","cju5o1vu9gz8a0818eyy92bns\n","cju5o4pk9h0720755lgp9jq8m\n","cju5tenjojp1j0755ms4949h2\n","cju5tgbzhjllu08174ca41eus\n","cju5thdbrjp1108715xdfx356\n","cju5u4pywk81x0817vn9pe14z\n","cju5u6wf0kh1t0755bg1ssixv\n","cju5u8gz4kj5b07552e2wpkwp\n","cju5udcufki0s09874ll1dbr5\n","cju5ufn3skquf0818dhapnhba\n","cju5uget8krjy0818kvywd0zu\n","cju5uhrdwkmsu0817ervv91l8\n","cju5ukkg6kv7u08011x2b6zl5\n","cju5uxjnol2r509871qv2yeia\n","cju5uzmaol56l0817flxh4w9p\n","cju5v8pgplg6k0755rvi2t63h\n","cju5vbo6jldrt0871jf6f1700\n","cju5vcmrqla7i0817x4sp4pqw\n","cju5vgawslbe30987ndeepc1b\n","cju5vi4nxlc530817uoqm2m7a\n","cju5von04litr08718j8po40a\n","cju5vutu7ll8w0871dfp92n9p\n","cju5vwbr4lhqn0987a1pji0ux\n","cju5vxuc5loxw0818u8xgf45p\n","cju5vzjoslpj708186z2fusmz\n","cju5w7xn0lrkq0801f9k0htgx\n","cju5waeduln160817w0agirve\n","cju5wcc90lu020850mjrxppv6\n","cju5wi6bqlxy90755bu227nvb\n","cju5wj0faly5008187n6530af\n","cju5wkonqlrl409877y8zvnub\n","cju5wmvsdlx1j0871npgj8j4b\n","cju5woy82m07m08505dmjg7g1\n","cju5wphwwlu3m0987hh3ltg88\n","cju5wqonpm0e60801z88ewmy1\n","cju5wrapcm2290818jsh26ppb\n","cju5wrrs0m2af0818vmnajbtw\n","cju5wtdu4m0im0871mix0yvc0\n","cju5wuhm1lwm40987vugqn3vv\n","cju5x00l6m5j608503k78ptee\n","cju5x15djm7ae0755h8czf6nt\n","cju5x28nzm7t907558ocq4bt7\n","cju5x7iskmad90818frchyfwd\n","cju5xjn5mm78b09871spyqhhr\n","cju5xkwzxmf0z0818gk4xabdm\n","cju5xneamme2p0801qdf7fdwv\n","cju5xopi0md7q0871r1sjc1av\n","cju5xq3tdm9fn0987pbedxdg5\n","cju5y4hgqmk0i08180rjhbwvp\n","cju5y7buemcw80987p0r30g9f\n","cju5y84q3mdv50817eyp82xf3\n","cju5yclrymlgj0818k426ud6z\n","cju5yeqiwmkgl0801fzv2douc\n","cju5ygh1zmmdi0755uod5e17i\n","cju5yhgznmkzb0801cji2vi8j\n","cju5yimthmlv80850zhoc90c2\n","cju5yjq1pmlgc0801z0t24bly\n","cju5ymyd8mmdc0801ry3by1xr\n","cju6ur9l9v9jq0755paud9uka\n","cju6us80mv1b50871ebyq2wxa\n","cju6ut4l8va6y0755tyw3vfqq\n","cju6uy20suzbl0987rzuhz7z9\n","cju6uzxk0v83p0801rcwnexdu\n","cju6v1m1xv07w09870ah3njy1\n","cju6v3bb2v7xo085090blubyw\n","cju6v4szov55u0871qmqz3v8n\n","cju6v5ilsv8hk0850rb5sgh6o\n","cju6v6g6kvdw007552x6mb0po\n","cju6vgdmivcvb08018fra5lnv\n","cju6vifjlv55z0987un6y4zdo\n","cju6vqarjv7yo0987q4b1btk1\n","cju6vrs1ov8cr098788h8gs6j\n","cju6vta3kvazg0817qbeppjtm\n","cju6vucxvvlda0755j7msqnya\n","cju6vvb8svhed0801jjcquh5e\n","cju6vvxsev9y30987kespucdg\n","cju6w733bveoz0817e600tw72\n","cju6wi3akvn8r0801px8eligc\n","cju6wjm81vgsc0987enk9n3pr\n","cju6wll7wvo3y08502pagos8m\n","cju6wn57mvooj0850rp78hhy7\n","cju6wt9jvvn500871hjn3t3g0\n","cju6wuojavt740818b5qcv3iw\n","cju6x0yqbvxqt0755dhxislgb\n","cju6x35ervu2808015c7eoqe4\n","cju6x4t13vyw60755gtcf9ndu\n","cju6x97w4vwua0850x0997r0a\n","cju6xa0qmvzun0818xjukgncj\n","cju6xifswvwbo0987nibtdr50\n","cju6xlygpw7bs0818n691jsq4\n","cju6xmqd9w0250817l5kxfnsk\n","cju6ywm40wdbo0987pbftsvtg\n","cju6yxyt0wh080871sqpepu47\n","cju6yywx1whbb0871ksgfgf9f\n","cju6z1bzbwfq50817b2alatvr\n","cju6z2616wqbk07555bvnuyr1\n","cju6z600qwh4z081700qimgl9\n","cju6z7e4bwgdd0987ogkzq9kt\n","cju6z9a9kwsl007552s49rx6i\n","cju76erapykj30871x5eaxh4q\n","cju76l27oyrw907551ri2a7fl\n","cju76lsehyia10987u54vn8rb\n","cju76o55nymqd0871h31sph9w\n","cju77196iyshb0850ycbto50a\n","cju772304yw5t0818vbw8kkjf\n","cju773hsyyosz0817pk1e7sjq\n","cju774fmayxif0818u2g79usw\n","cju7787c5yy3l080159mwqsnj\n","cju77afzlz3kp07550x5nafzs\n","cju77b3wyz4160755qis4ljsb\n","cju77bvg0yv4r0987yh60xmjo\n","cju77g99iyxc00817zqi2ppor\n","cju77idwfz36d0871tzfzz51i\n","cju77j66ez52p08019xygi0co\n","cju77k828z46w0871r0avuoo9\n","cju77q10sz9ug0801449wu1nu\n","cju77re6fz5bb0817vp9redjg\n","cju77t0razbvm080106o56289\n","cju77u1sjz77b0817ft44r3fk\n","cju77vvcwzcm50850lzoykuva\n","cju783tmkzkqu081803g7q5vk\n","cju784jpdzeae0987q5ypq883\n","cju785htizjzo08017tvlhtg4\n","cju787jnjzjuj0871p94nck9g\n","cju7adqyj1jcx08712r1ro5gx\n","cju7ae7bq1f820987toc8si1d\n","cju7aez2x1jtj0871ztezs3oi\n","cju7afqon1ip40850ue2308b6\n","cju7agj961l2r0818z29iq8yn\n","cju7ahtkb1jr90801jck4kbds\n","cju7aifyo1p3n07552nxjx51f\n","cju7ajnbo1gvm098749rdouk0\n","cju7aklv31h4309871m29l4e7\n","cju7alcgr1lsr0871riqk84z7\n","cju7amjna1ly40871ugiokehb\n","cju7ap09p1kz10850ldccjebj\n","cju7apr0c1qqm0755s7msqot4\n","cju7aqkue1i2k09879uzcpt8r\n","cju7arvfe1ldu0850erdmphgj\n","cju7atnm31if40817pqclnjer\n","cju7avvi51iox0817ym55y6tt\n","cju7awzmu1ncs0871hziy65zx\n","cju7azuu31mia0801pf9ib5ed\n","cju7b10ce1mnm08011c5bwyr4\n","cju7b1ygu1msd0801hywhy0mc\n","cju7b2l561oas0871decgslaf\n","cju7b3f5h1sm40755i572jden\n","cju7b4mtw1n9n080186209f3d\n","cju7b5afm1nfw0801xqm8bf8q\n","cju7b9vcs1luz0987ta60j1dy\n","cju7bb3ss1uoo0755pmhyco7t\n","cju7bc95p1mdm0817yqj5jc6j\n","cju7bd1qu1mx409877xjxibox\n","cju7bduyq1rjf08719giru9ho\n","cju7bf1lp1shi081835vs84lc\n","cju7bfx651qr80801cs7epotb\n","cju7bgnvb1sf808717qa799ir\n","cju7bmi1v1pnj0987pa52jjok\n","cju7cl8zm1xcu0817ado0jpas\n","cju7cp6dw244p0818gncdol4m\n","cju7cq6su27qv075574dir0r3\n","cju7crgxa28550755wbsgqkel\n","cju7csvlb22fr0850lvm45n3x\n","cju7ctvqn25dy08186g442m1r\n","cju7cue9b232j0801qdzk1ykj\n","cju7cufm7298k0755j09uf3of\n","cju7d1tvt25bu08019dvw3uff\n","cju7d2q1k27nf08715zshsckt\n","cju7d3oc82cho0755dajlwldz\n","cju7d4jk723eu0817bqz2n39m\n","cju7d5m0p23kn09871rk7pu3v\n","cju7d6ux323ze0987xos3srkx\n","cju7d7aut2a2p0818z4uxc6cd\n","cju7d7tly27h408016fyp5nr7\n","cju7d8m3b2e210755l8fj1yph\n","cju7d9seq29zd0871nzl2uu5m\n","cju7da88w2eod0755wejzynvt\n","cju7db7lp2f400755tntd1ohf\n","cju7dbppn28nx085097654msi\n","cju7dda8w2br20818zhsuz8s7\n","cju7ddtz729960801uazp1knc\n","cju7deifq2fzn0755lc8idyh8\n","cju7dff9529h208503w60lbil\n","cju7dglf226g50987ohbthl19\n","cju7dhpsc2dnn0818025m6857\n","cju7dizi82h2i0755doucgnt3\n","cju7dlk532dsh0871zvr6qz0r\n","cju7dmlgf2ebw0871ieqas5fh\n","cju7dn24o296i09871qfxb8s2\n","cju7do8c72dbo0801vxfzxdc4\n","cju7ehljc2or70871261br8ai\n","cju7ejm2l2ncl0801wq6y84nw\n","cju7ekbo32pft0871fv7kzwb9\n","cju7el6xv2k520817qxx9wdr5\n","cju7emdni2py40871ivhxjtut\n","cju7eotqi2qea0871y8yc7tqh\n","cju7epwj82koz098713apjnzo\n","cju7er4kc2opa0801anuxc0eb\n","cju7es23b2vcp0755gpbm9s7v\n","cju7et17a2vjk0755e743npl1\n","cju7etr3y2p4t0801cdzjj8ab\n","cju7eueum2oqn0850rodmx8zo\n","cju7ev2b12owa08500bpfpwyw\n","cju7evxt12m730987rxivne3x\n","cju7ey10f2rvf0871bwbi9x82\n","cju7ez7r22qbc08015xfoz2wb\n","cju7ezs7g2mxm098787atbran\n","cju7f0ec32txj08184asb8w5f\n","cju7f4sc62xqj075597xpmuoy\n","cju7f5ghb2r5s0801chwkxxh9\n","cju7f6cqy2ur20818t1saazbm\n","cju7f900s2o0k08175gl1giid\n","cju7f9umg2olj0987fj5y285w\n","cju7fazv92ywx0755xov2erga\n","cju7fbndk2sl608015ravktum\n","cju7fcgbe2z3p07550vaflqdb\n","cju7fd6yt2p740987wkr8exo1\n","cju7fen322ou10817ziqkob4k\n","cju7ff97z2ow40817u2r83my5\n","cju7flevb2wii08188otgs9p2\n","cju7fmvpk2q170987v6i3ola8\n","cju7fnfv02tt90801djnix9m8\n","cju7fob3x301u0755x985pmmq\n","cju7fpfzq2wyf0818xxd1oziv\n","cju7fq7mm2pw508176uk5ugtx\n","cju7frtqu2xa20818wq8r9fzf\n","cju7g7ba42z310987bqzbi2bq\n","cju83h9ysjwe808716nt35oah\n","cju83ipu3jwpx0801z5pvguf8\n","cju83k8fyjsxr0817d6nxs6r4\n","cju83kxitjv340987z09m0ezy\n","cju83mki1jv5w0817kubxm31r\n","cju83nwu1jxte0987h1krpfmv\n","cju83qd0yjyht0817ktkfl268\n","cju83rcnzkbsj0755x5anfrcg\n","cju83syhdk6gs0801rf1rekdl\n","cju83u9ftk3ni0987qnhlcinv\n","cju83vvmik9wa08710yeh7cuk\n","cju83wwn1k55e0850kw6i2d81\n","cju83yddek68q0850d2x7zfkm\n","cju8402x1kcy70801t6kz6bdi\n","cju8418jhkf7d0818ga2v0xq0\n","cju8432cmkgq90871cxe4iptl\n","cju843yjskhq30818qre4rwm2\n","cju846ec0kj7z08012o10klrb\n","cju847pxykriq0755268ktrk2\n","cju849c23kgnk0817cgv2hw1e\n","cju84aoa3ktwn0755pfl4gfwd\n","cju84dsvaklpx098750hp83x4\n","cju84ffdzkrjn08183jh1fxmb\n","cju84gpefknwm098714oq8q61\n","cju84hibuktj80871u519o71q\n","cju84ih17kp5l09876bkooocl\n","cju84jdl9kv0i0871eog9b3i9\n","cju84kplnl1y30755ropua1b0\n","cju8567gdlcbq0801dwwyo2jt\n","cju857ad0l88m0817qx4cwxnf\n","cju858eswlepn0871pzvdrhj1\n","cju85a8h8llwm07559wxg4t5w\n","cju85bf1algsq0871y9gtlq97\n","cju85c2d4ln1b0755zz1z3onx\n","cju85citjlnfm0755i4rk5tqj\n","cju85dx63lic408017f0l0400\n","cju85fc11ljr40818edpb0inh\n","cju85hguellg50818kwu3s8d1\n","cju85ia1slh220987y7c20sm2\n","cju85je7vlht70817c9jcjwi4\n","cju85l4yjlops0801fvmnwptf\n","cju85mpuglq8k0818d2it6hzb\n","cju885ikhnmkn09878s2lqtuh\n","cju886ryxnsl50801r93jai7q\n","cju887ftknop008177nnjt46y\n","cju888fr7nveu0818r9uwtiit\n","cju88aq6vo1ij0755c2ey7z7n\n","cju88cddensj00987788yotmg\n","cju88evxanv9r08176zkeovec\n","cju88fpm4o0tl0871w1i6a4ds\n","cju88gx09o2vk0818610zody3\n","cju88itqbny720987hxizbj5y\n","cju88k75inzyb0850ccv5x3vk\n","cju88l66no10s0850rsda7ej1\n","cju88msmoo3470817m441j4sg\n","cju88nroho44508500129f1nh\n","cju88oh0po9gq0801nge4tgr1\n","cju88q6h6obpd0871ckmiabbo\n","cju88rl5eo94l0850kf5wtrm1\n","cju88t4fvokxf07558ymyh281\n","cju88trl3ogi208716qvti51b\n","cju88v2f9oi8w0871hx9auh01\n","cju88vx2uoocy075531lc63n3\n","cju88y1mwoln50871emyfny1g\n","cju88z8bson4h0871nnd7fdxo\n","cju890guyoiti098753yg6cdu\n","cju8914beokbf0850isxpocrk\n","cju892fesoq2g0801n0e0jyia\n","cju893jmdompz0817xn3g1w4h\n","cju89y9h0puti0818i5yw29e6\n","cju89z6pqpqfx0817mfv8ixjc\n","cju8a1jtvpt9m081712iwkca7\n","cju8a2itsq4dv0755ntlovpxe\n","cju8a3nhbpwnb0850d37fo2na\n","cju8a56vxpy780850r45yu4wk\n","cju8a84g0q76m0818hwiggkod\n","cju8abobpqbir08189u01huru\n","cju8adb60qbiu080188mxpf8d\n","cju8aeei7q8k308173n9y4klv\n","cju8aj01yqeqm0850lhdz3xdw\n","cju8alhigqn2h0801zksudldd\n","cju8amfdtqi4x09871tygrgqe\n","cju8ando2qqdo0818ck7i1be1\n","cju8apjewqrk00801k5d71gky\n","cju8aqq8uqmoq0987hphto9gg\n","cju8arof2qpf20850ifr1bnqj\n","cju8ashhnquqr0801rwduzt7d\n","cju8at3s1qqqx0850hcq8nmnq\n","cju8auylgqx0z0871u4o4db7o\n","cju8aw9n1qyg10801jkjlmors\n","cju8axq24r4an0755yhv9d4ly\n","cju8ayeq7r1fb0818z1junacy\n","cju8azmhcr66e0755t61atz72\n","cju8b0jr0r2oi0801jiquetd5\n","cju8b1v3br45u087189kku66u\n","cju8b2rmgr52s0801p54eyflx\n","cju8b3ka8r64u0801fh18hk7l\n","cju8b4ja9r2s808509d45ma86\n","cju8b542nr81x0871uxnkm9ih\n","cju8b5p40r2c60987ofa0mu03\n","cju8b6rp0r5st0850184f79xt\n","cju8b7aqtr4a00987coba14b7\n","cju8b8yair65w09878pyqtr96\n","cju8bafgqrf4x0818twisk3ea\n","cju8bbznkrf5g0871jncffynk\n","cju8bff9nrfi10850fmfzbf8v\n","cju8bgdmqrksy0801tozdmraa\n","cju8bh8surexp0987o5pzklk1\n","cju8bi8q7rlmn0871abc5ch8k\n","cju8bj2ssrmlm0871gc2ug2rs\n","cju8bk8oirjhw0817hgkua2w8\n","cju8bljw9rqk20801kr54akrl\n","cju8bm24yrrdp081829mbo8ic\n","cju8bn7m2rmm70817hgxpb1uq\n","cju8bop5jrsid08716i24fqda\n","cju8bpctzrqkr0850zeldv9kt\n","cju8bqxxurs6i0850mu7mtef9\n","cju8brv16rx7f0818uf5n89pv\n","cju8bssulrrcy0987h1vq5060\n","cju8buos5rz9b08715lfr0f4f\n","cju8bw697rwg308177tg8huas\n","cju8bysfgrzkl081786jwac09\n","cju8bzzy2s66m08016z6mouqt\n","cju8c1a0ws7o208181c6lbsom\n","cju8c2rqzs5t80850d0zky5dy\n","cju8c3xs7sauj0801ieyzezr5\n","cju8c5223s8j80850b4kealt4\n","cju8c5mxls96t0850wvkvsity\n","cju8c5zcbsdfz0801o5t6jag1\n","cju8c6hnxsdvr0801wn0vrsa6\n","cju8c82iosagu0817l74s4m5g\n","cju8c9akjsdjj0850s67uzlxq\n","cju8ca4geseia0850i2ru11hw\n","cju8cattbsivm0818p446wgel\n","cju8cbsyssiqj0871gr4jedjp\n","cju8cdeazsm8h0801jxifmzur\n","cju7efffp2ivf0817etg3jehl\n"]}]},{"cell_type":"code","source":["import shutil\n","from pathlib import Path\n","\n","# Source and target directories\n","source_dir = Path(\"/content/kvasirseg\")\n","target_base = Path(\"/content/YOLO_SAM2/Kvasir\")\n","\n","# Make sure target folders exist\n","(target_base / \"train/images\").mkdir(parents=True, exist_ok=True)\n","(target_base / \"train/labels\").mkdir(parents=True, exist_ok=True)\n","(target_base / \"valid/images\").mkdir(parents=True, exist_ok=True)\n","(target_base / \"valid/labels\").mkdir(parents=True, exist_ok=True)\n","\n","# Function to copy files from list\n","def copy_files(file, split):\n","    with open(file, 'r') as f:\n","        lines = f.read().splitlines()\n","\n","    for line in lines:\n","        img_file = source_dir / f\"{line}.jpg\"\n","        csv_file = source_dir / f\"{line}.csv\"\n","\n","        img_target = target_base / split / \"images\" / f\"{line}.jpg\"\n","        csv_target = target_base / split / \"labels\" / f\"{line}.csv\"\n","\n","        if img_file.exists():\n","            shutil.copy(img_file, img_target)\n","        else:\n","            print(f\"Image missing: {img_file}\")\n","\n","        if csv_file.exists():\n","            shutil.copy(csv_file, csv_target)\n","        else:\n","            print(f\"Label missing: {csv_file}\")\n","\n","# Copy training and validation data\n","copy_files(\"/content/kvasirseg/train.txt\", \"train\")\n","copy_files(\"/content/kvasirseg/val.txt\", \"valid\")\n","\n","print(\"✅ Done organizing data.\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"3x8NwE_Hb-QU","executionInfo":{"status":"ok","timestamp":1754418356288,"user_tz":-345,"elapsed":423,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"83ef0407-b031-47f6-ece2-220f09e993b1"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Done organizing data.\n"]}]},{"cell_type":"code","source":["!pip install ultralytics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"xK0hSjneXgzh","executionInfo":{"status":"ok","timestamp":1754416532260,"user_tz":-345,"elapsed":89828,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"afa83a36-3507-464e-e2e7-3c6b0f70773a"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ultralytics\n","  Downloading ultralytics-8.3.174-py3-none-any.whl.metadata (37 kB)\n","Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.0.2)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.12.0.88)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.3.0)\n","Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.3)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.16.0)\n","Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.6.0+cu124)\n","Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.21.0+cu124)\n","Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.67.1)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (5.9.5)\n","Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0)\n","Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2)\n","Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n","  Downloading ultralytics_thop-2.0.15-py3-none-any.whl.metadata (14 kB)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.59.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.3)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2025.7.14)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.18.0)\n","Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (4.14.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.5)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2025.3.0)\n","Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (0.6.2)\n","Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2.21.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127)\n","Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.8.0->ultralytics)\n","  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.2.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)\n","Downloading ultralytics-8.3.174-py3-none-any.whl (1.0 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m59.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m78.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m37.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m45.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading ultralytics_thop-2.0.15-py3-none-any.whl (28 kB)\n","Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n","  Attempting uninstall: nvidia-nvjitlink-cu12\n","    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n","    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n","      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n","  Attempting uninstall: nvidia-curand-cu12\n","    Found existing installation: nvidia-curand-cu12 10.3.6.82\n","    Uninstalling nvidia-curand-cu12-10.3.6.82:\n","      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n","  Attempting uninstall: nvidia-cufft-cu12\n","    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n","    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n","      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n","  Attempting uninstall: nvidia-cuda-runtime-cu12\n","    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n","    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n","      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n","  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n","    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n","    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n","      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n","  Attempting uninstall: nvidia-cuda-cupti-cu12\n","    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n","    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n","      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n","  Attempting uninstall: nvidia-cublas-cu12\n","    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n","    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n","      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n","  Attempting uninstall: nvidia-cusparse-cu12\n","    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n","    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n","      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n","  Attempting uninstall: nvidia-cudnn-cu12\n","    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n","    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n","      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n","  Attempting uninstall: nvidia-cusolver-cu12\n","    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n","    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n","      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n","Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.174 ultralytics-thop-2.0.15\n"]}]},{"cell_type":"code","source":["%cd /content/YOLO_SAM2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wOF2GMYUXrHB","executionInfo":{"status":"ok","timestamp":1754416537949,"user_tz":-345,"elapsed":19,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"4f40de50-b7ab-4706-d21f-443edac2c88c"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/YOLO_SAM2\n"]}]},{"cell_type":"code","source":["#on Creat_YAML.py\n","#replace line 30 to 33 with lines below\n","\n","# import os\n","\n","# os.makedirs(\"./Kvasir/train/images\", exist_ok=True)\n","# os.makedirs(\"./Kvasir/train/labels\", exist_ok=True)\n","# os.makedirs(\"./Kvasir/valid/images\", exist_ok=True)\n","# os.makedirs(\"./Kvasir/valid/labels\", exist_ok=True)\n","\n","#change line 50 to  for i in range(len(image_list)): from for i in range(800):\n","\n","#comment all the code from line 83 to 854\n","\n"],"metadata":{"id":"gjRwAzT_YuUl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python Creat_YAML.py --dataset Kvasir"],"metadata":{"id":"7LIYXU_ZX-oj","executionInfo":{"status":"ok","timestamp":1754417073247,"user_tz":-345,"elapsed":3715,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["!yolo task=detect mode=train model=yolov8l.pt imgsz=640 data=./YAML/Kvasir.yaml epochs=50 batch=0.90 name=Kvasir"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ng52xPY2a1Yl","executionInfo":{"status":"ok","timestamp":1754418634409,"user_tz":-345,"elapsed":242797,"user":{"displayName":"Santosh Upreti","userId":"15542637101614503540"}},"outputId":"2713ffec-56ce-4bcb-ac74-4aa90933607f"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=0.9, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=./YAML/Kvasir.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8l.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=Kvasir2, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/Kvasir2, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n","Overriding model.yaml nc=80 with nc=1\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n","  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n","  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n","  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n","  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n","  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n","  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n","  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n","  8                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           \n","  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n"," 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 12                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n"," 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 15                  -1  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 \n"," 16                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n"," 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 18                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 \n"," 19                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n"," 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 21                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n"," 22        [15, 18, 21]  1   5583571  ultralytics.nn.modules.head.Detect           [1, [256, 512, 512]]          \n","Model summary: 209 layers, 43,630,611 parameters, 43,630,595 gradients, 165.4 GFLOPs\n","\n","Transferred 589/595 items from pretrained weights\n","Freezing layer 'model.22.dfl.conv.weight'\n","\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1902.8±514.6 MB/s, size: 94.5 KB)\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/YOLO_SAM2/Kvasir/train/labels... 0 images, 880 backgrounds, 0 corrupt: 100% 880/880 [00:00<00:00, 4612.57it/s]\n","WARNING ⚠️ \u001b[34m\u001b[1mtrain: \u001b[0mNo labels found in /content/YOLO_SAM2/Kvasir/train/labels.cache. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/YOLO_SAM2/Kvasir/train/labels.cache\n","WARNING ⚠️ Labels are missing or empty in /content/YOLO_SAM2/Kvasir/train/labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 90.0% CUDA memory utilization.\n","\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (Tesla T4) 14.74G total, 0.39G reserved, 0.37G allocated, 13.98G free\n","      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n","    43630611       165.4         1.395         54.64           231        (1, 3, 640, 640)                    list\n","    43630611       330.8         2.210         54.94         133.4        (2, 3, 640, 640)                    list\n","    43630611       661.6         3.601         66.36         137.8        (4, 3, 640, 640)                    list\n","    43630611        1323         5.952           120         212.7        (8, 3, 640, 640)                    list\n","    43630611        2646        11.201         242.7         401.3       (16, 3, 640, 640)                    list\n","\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 18 for CUDA:0 13.25G/14.74G (90%) ✅\n","\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1947.6±653.2 MB/s, size: 64.4 KB)\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/YOLO_SAM2/Kvasir/train/labels.cache... 0 images, 880 backgrounds, 0 corrupt: 100% 880/880 [00:00<?, ?it/s]\n","WARNING ⚠️ Labels are missing or empty in /content/YOLO_SAM2/Kvasir/train/labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 860.5±501.6 MB/s, size: 79.3 KB)\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/YOLO_SAM2/Kvasir/valid/labels... 0 images, 120 backgrounds, 0 corrupt: 100% 120/120 [00:00<00:00, 4118.73it/s]\n","WARNING ⚠️ \u001b[34m\u001b[1mval: \u001b[0mNo labels found in /content/YOLO_SAM2/Kvasir/valid/labels.cache. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/YOLO_SAM2/Kvasir/valid/labels.cache\n","WARNING ⚠️ Labels are missing or empty in /content/YOLO_SAM2/Kvasir/valid/labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance.\n","Plotting labels to runs/detect/Kvasir2/labels.jpg... \n","WARNING ⚠️ zero-size array to reduction operation maximum which has no identity\n","\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n","\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005625000000000001), 103 bias(decay=0.0)\n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1mruns/detect/Kvasir2\u001b[0m\n","Starting training for 50 epochs...\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       1/50      10.8G          0      259.2          0          0        640: 100% 49/49 [00:41<00:00,  1.19it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.10it/s]\n","/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:850: RuntimeWarning: Mean of empty slice.\n","  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n","/usr/local/lib/python3.11/dist-packages/numpy/_core/_methods.py:130: RuntimeWarning: invalid value encountered in divide\n","  ret = um.true_divide(\n","                   all        120          0          0          0          0          0\n","WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       2/50      10.5G          0      1.655          0          0        640: 100% 49/49 [00:40<00:00,  1.20it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  2.00it/s]\n","/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:850: RuntimeWarning: Mean of empty slice.\n","  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n","/usr/local/lib/python3.11/dist-packages/numpy/_core/_methods.py:130: RuntimeWarning: invalid value encountered in divide\n","  ret = um.true_divide(\n","                   all        120          0          0          0          0          0\n","WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       3/50      10.7G          0   0.002953          0          0        640: 100% 49/49 [00:40<00:00,  1.20it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.04it/s]\n","/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:850: RuntimeWarning: Mean of empty slice.\n","  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n","/usr/local/lib/python3.11/dist-packages/numpy/_core/_methods.py:130: RuntimeWarning: invalid value encountered in divide\n","  ret = um.true_divide(\n","                   all        120          0          0          0          0          0\n","WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       4/50      10.7G          0  0.0002064          0          0        640: 100% 49/49 [00:40<00:00,  1.22it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.97it/s]\n","/usr/local/lib/python3.11/dist-packages/ultralytics/utils/metrics.py:850: RuntimeWarning: Mean of empty slice.\n","  i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index\n","/usr/local/lib/python3.11/dist-packages/numpy/_core/_methods.py:130: RuntimeWarning: invalid value encountered in divide\n","  ret = um.true_divide(\n","                   all        120          0          0          0          0          0\n","WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       5/50      10.5G          0  1.703e-06          0          0        640:  29% 14/49 [00:12<00:30,  1.15it/s]\n","Traceback (most recent call last):\n","  File \"/usr/local/bin/yolo\", line 8, in <module>\n","    sys.exit(entrypoint())\n","             ^^^^^^^^^^^^\n","  File \"/usr/local/lib/python3.11/dist-packages/ultralytics/cfg/__init__.py\", line 983, in entrypoint\n","    getattr(model, mode)(**overrides)  # default args from model\n","    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n","  File \"/usr/local/lib/python3.11/dist-packages/ultralytics/engine/model.py\", line 799, in train\n","    self.trainer.train()\n","  File \"/usr/local/lib/python3.11/dist-packages/ultralytics/engine/trainer.py\", line 227, in train\n","    self._do_train(world_size)\n","  File \"/usr/local/lib/python3.11/dist-packages/ultralytics/engine/trainer.py\", line 435, in _do_train\n","    pbar.set_description(\n","  File \"/usr/local/lib/python3.11/dist-packages/tqdm/std.py\", line 1382, in set_description\n","    def set_description(self, desc=None, refresh=True):\n","\n","KeyboardInterrupt\n"]}]}]}