Datasets:
File size: 203,718 Bytes
d9c6a82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"sns.set_style(\"whitegrid\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load in LILA CSV from [this commit](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-test-sets/lila_image_urls_and_labels.csv). (this will take a while)\n",
"\n",
"sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455\n",
"size 15931383983"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>suborder</th>\n",
" <th>infraorder</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Caltech Camera Traps</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3...</td>\n",
" <td>Caltech Camera Traps : 6f2160eb-5567-11e8-990e...</td>\n",
" <td>Caltech Camera Traps : 26</td>\n",
" <td>1</td>\n",
" <td>empty</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Caltech Camera Traps</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...</td>\n",
" <td>Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...</td>\n",
" <td>Caltech Camera Traps : 26</td>\n",
" <td>1</td>\n",
" <td>deer</td>\n",
" <td>odocoileus</td>\n",
" <td>...</td>\n",
" <td>ruminantia</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>cervidae</td>\n",
" <td>capreolinae</td>\n",
" <td>odocoileini</td>\n",
" <td>odocoileus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Caltech Camera Traps</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
" <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
" <td>Caltech Camera Traps : 38</td>\n",
" <td>2</td>\n",
" <td>cat</td>\n",
" <td>felis catus</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>felidae</td>\n",
" <td>felinae</td>\n",
" <td>NaN</td>\n",
" <td>felis</td>\n",
" <td>felis catus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Caltech Camera Traps</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
" <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
" <td>Caltech Camera Traps : 38</td>\n",
" <td>2</td>\n",
" <td>opossum</td>\n",
" <td>didelphis virginiana</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>didelphidae</td>\n",
" <td>didelphinae</td>\n",
" <td>didelphini</td>\n",
" <td>didelphis</td>\n",
" <td>didelphis virginiana</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Caltech Camera Traps</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3...</td>\n",
" <td>Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb...</td>\n",
" <td>Caltech Camera Traps : 33</td>\n",
" <td>2</td>\n",
" <td>empty</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_name url_gcp \\\n",
"0 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n",
"1 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n",
"2 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n",
"3 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n",
"4 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n",
"\n",
" url_aws \\\n",
"0 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"1 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"2 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"3 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"4 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"\n",
" url_azure \\\n",
"0 https://lilawildlife.blob.core.windows.net/lil... \n",
"1 https://lilawildlife.blob.core.windows.net/lil... \n",
"2 https://lilawildlife.blob.core.windows.net/lil... \n",
"3 https://lilawildlife.blob.core.windows.net/lil... \n",
"4 https://lilawildlife.blob.core.windows.net/lil... \n",
"\n",
" image_id \\\n",
"0 Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3... \n",
"1 Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3... \n",
"2 Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3... \n",
"3 Caltech Camera Traps : 59641f56-23d2-11e8-a6a3... \n",
"4 Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3... \n",
"\n",
" sequence_id \\\n",
"0 Caltech Camera Traps : 6f2160eb-5567-11e8-990e... \n",
"1 Caltech Camera Traps : 6f27ed66-5567-11e8-9e92... \n",
"2 Caltech Camera Traps : 6f04895c-5567-11e8-a3d6... \n",
"3 Caltech Camera Traps : 6f0385b5-5567-11e8-a80b... \n",
"4 Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb... \n",
"\n",
" location_id frame_num original_label scientific_name \\\n",
"0 Caltech Camera Traps : 26 1 empty NaN \n",
"1 Caltech Camera Traps : 26 1 deer odocoileus \n",
"2 Caltech Camera Traps : 38 2 cat felis catus \n",
"3 Caltech Camera Traps : 38 2 opossum didelphis virginiana \n",
"4 Caltech Camera Traps : 33 2 empty NaN \n",
"\n",
" ... suborder infraorder superfamily family subfamily \\\n",
"0 ... NaN NaN NaN NaN NaN \n",
"1 ... ruminantia NaN NaN cervidae capreolinae \n",
"2 ... NaN NaN NaN felidae felinae \n",
"3 ... NaN NaN NaN didelphidae didelphinae \n",
"4 ... NaN NaN NaN NaN NaN \n",
"\n",
" tribe genus species subspecies variety \n",
"0 NaN NaN NaN NaN NaN \n",
"1 odocoileini odocoileus NaN NaN NaN \n",
"2 NaN felis felis catus NaN NaN \n",
"3 didelphini didelphis didelphis virginiana NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 32 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", low_memory = False)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['dataset_name', 'url_gcp', 'url_aws', 'url_azure', 'image_id',\n",
" 'sequence_id', 'location_id', 'frame_num', 'original_label',\n",
" 'scientific_name', 'common_name', 'datetime', 'annotation_level',\n",
" 'kingdom', 'phylum', 'subphylum', 'superclass', 'class', 'subclass',\n",
" 'infraclass', 'superorder', 'order', 'suborder', 'infraorder',\n",
" 'superfamily', 'family', 'subfamily', 'tribe', 'genus', 'species',\n",
" 'subspecies', 'variety'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"annotation_level\n",
"sequence 16807793\n",
"image 3533538\n",
"unknown 3382215\n",
"Name: count, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.annotation_level.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Annotation level indicates image vs sequence (or unknown), we specifically want those annotated at the image-level, since they should be \"clean\" images. Though we will want to label them with how many distinct species are in the image first.\n",
"\n",
"We have 3,533,538 images labeled to the image-level.\n",
"\n",
"### Check Dataset Counts\n",
"\n",
"1. Make sure we have all datasets expected. We're specifically interested in:\n",
" - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/)\n",
" - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)\n",
" - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)\n",
" - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)\n",
" - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps)\n",
" - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)\n",
" - [NOAA Puget Sound Nearshore Fish 2017-2018](https://lila.science/datasets/noaa-puget-sound-nearshore-fish) could be interesting for the combined categories, though it is _very_ general (has only three labels: `fish`, `crab`, `fish_and_crab`).\n",
"2. Check which/how many datasets are labeled to the image level (and check for match to [Andrey's spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link))."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dataset_name\n",
"Snapshot Serengeti 7261545\n",
"Snapshot Safari 2024 Expansion 4097015\n",
"NACTI 3382215\n",
"Trail Camera Images of New Zealand Animals 2453840\n",
"SWG Camera Traps 2039657\n",
"Idaho Camera Traps 1551552\n",
"WCS Camera Traps 1369953\n",
"Wellington Camera Traps 270450\n",
"Channel Islands Camera Traps 245644\n",
"Caltech Camera Traps 243177\n",
"UNSW Predators 131802\n",
"Island Conservation Camera Traps 128207\n",
"Ohio Small Animals 118554\n",
"Orinoquia Camera Traps 112267\n",
"Snapshot Mountain Zebra 73606\n",
"Desert Lion Conservation Camera Traps 63468\n",
"Snapshot Karoo 38320\n",
"Snapshot Camdeboo 30717\n",
"Snapshot Enonkishu 30542\n",
"Seattle(ish) Camera Traps 25019\n",
"Missouri Camera Traps 24673\n",
"Snapshot Kruger 10637\n",
"Snapshot Kgalagadi 10402\n",
"ENA24 10284\n",
"Name: count, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dataset_name.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dataset_name annotation_level\n",
"Caltech Camera Traps image 243177\n",
"Channel Islands Camera Traps image 245644\n",
"Desert Lion Conservation Camera Traps image 63468\n",
"ENA24 image 10284\n",
"Idaho Camera Traps sequence 1551552\n",
"Island Conservation Camera Traps image 128207\n",
"Missouri Camera Traps sequence 23397\n",
" image 1276\n",
"NACTI unknown 3382215\n",
"Ohio Small Animals image 118554\n",
"Orinoquia Camera Traps image 112267\n",
"SWG Camera Traps sequence 2039657\n",
"Seattle(ish) Camera Traps image 25019\n",
"Snapshot Camdeboo sequence 30717\n",
"Snapshot Enonkishu sequence 30542\n",
"Snapshot Karoo sequence 38320\n",
"Snapshot Kgalagadi sequence 10402\n",
"Snapshot Kruger sequence 10637\n",
"Snapshot Mountain Zebra sequence 73606\n",
"Snapshot Safari 2024 Expansion sequence 4097015\n",
"Snapshot Serengeti sequence 7261545\n",
"Trail Camera Images of New Zealand Animals image 2453840\n",
"UNSW Predators image 131802\n",
"WCS Camera Traps sequence 1369953\n",
"Wellington Camera Traps sequence 270450\n",
"Name: count, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby([\"dataset_name\"]).annotation_level.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems snapshot safari exapansion and SWG camera traps are not labeled at the image level, despite the indication in the spreadsheet...\n",
"\n",
"The NOAA one isn't here, but that's okay. Let's also take a look at [ENA24](https://lila.science/datasets/ena24detection).\n",
"\n",
"We'll subset to just the 7 identified, though we'll likely not continue with Snapshot Safari and SWG, since we want to make sure the test set labels are accurate."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"datasets_of_interest = [\"Desert Lion Conservation Camera Traps\",\n",
" \"Island Conservation Camera Traps\",\n",
" \"Ohio Small Animals\",\n",
" \"Orinoquia Camera Traps\",\n",
" \"SWG Camera Traps\",\n",
" \"Snapshot Safari 2024 Expansion\",\n",
" \"ENA24\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>suborder</th>\n",
" <th>infraorder</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>243177</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>bird</td>\n",
" <td>aves</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243178</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 10</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>bird</td>\n",
" <td>aves</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243179</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 100</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern gray squirrel</td>\n",
" <td>sciurus carolinensis</td>\n",
" <td>...</td>\n",
" <td>sciuromorpha</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>sciurinae</td>\n",
" <td>sciurini</td>\n",
" <td>sciurus</td>\n",
" <td>sciurus carolinensis</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243180</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1000</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern chipmunk</td>\n",
" <td>tamias striatus</td>\n",
" <td>...</td>\n",
" <td>sciuromorpha</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>xerinae</td>\n",
" <td>tamiini</td>\n",
" <td>tamias</td>\n",
" <td>tamias striatus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243181</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1001</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern chipmunk</td>\n",
" <td>tamias striatus</td>\n",
" <td>...</td>\n",
" <td>sciuromorpha</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>xerinae</td>\n",
" <td>tamiini</td>\n",
" <td>tamias</td>\n",
" <td>tamias striatus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_name url_gcp \\\n",
"243177 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243178 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243179 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243180 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243181 ENA24 https://storage.googleapis.com/public-datasets... \n",
"\n",
" url_aws \\\n",
"243177 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243178 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243179 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243180 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243181 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"\n",
" url_azure image_id \\\n",
"243177 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1 \n",
"243178 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 10 \n",
"243179 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 100 \n",
"243180 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1000 \n",
"243181 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1001 \n",
"\n",
" sequence_id location_id frame_num original_label \\\n",
"243177 ENA24 : unknown ENA24 -1 bird \n",
"243178 ENA24 : unknown ENA24 -1 bird \n",
"243179 ENA24 : unknown ENA24 -1 eastern gray squirrel \n",
"243180 ENA24 : unknown ENA24 -1 eastern chipmunk \n",
"243181 ENA24 : unknown ENA24 -1 eastern chipmunk \n",
"\n",
" scientific_name ... suborder infraorder superfamily \\\n",
"243177 aves ... NaN NaN NaN \n",
"243178 aves ... NaN NaN NaN \n",
"243179 sciurus carolinensis ... sciuromorpha NaN NaN \n",
"243180 tamias striatus ... sciuromorpha NaN NaN \n",
"243181 tamias striatus ... sciuromorpha NaN NaN \n",
"\n",
" family subfamily tribe genus species \\\n",
"243177 NaN NaN NaN NaN NaN \n",
"243178 NaN NaN NaN NaN NaN \n",
"243179 sciuridae sciurinae sciurini sciurus sciurus carolinensis \n",
"243180 sciuridae xerinae tamiini tamias tamias striatus \n",
"243181 sciuridae xerinae tamiini tamias tamias striatus \n",
"\n",
" subspecies variety \n",
"243177 NaN NaN \n",
"243178 NaN NaN \n",
"243179 NaN NaN \n",
"243180 NaN NaN \n",
"243181 NaN NaN \n",
"\n",
"[5 rows x 32 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduced_df = df.loc[df[\"dataset_name\"].isin(datasets_of_interest)].copy()\n",
"reduced_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observe that we also now get multiple URL options; `url_aws` will likely be best/fastest for use with [`distributed-downloader`](https://github.com/Imageomics/distributed-downloader) to get the images."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOStream.flush timed out\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 6569452 entries, 243177 to 23566724\n",
"Data columns (total 32 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 dataset_name 6569452 non-null object\n",
" 1 url_gcp 6569452 non-null object\n",
" 2 url_aws 6569452 non-null object\n",
" 3 url_azure 6569452 non-null object\n",
" 4 image_id 6569452 non-null object\n",
" 5 sequence_id 6569452 non-null object\n",
" 6 location_id 6569452 non-null object\n",
" 7 frame_num 6569452 non-null int64 \n",
" 8 original_label 6569452 non-null object\n",
" 9 scientific_name 2884628 non-null object\n",
" 10 common_name 2884628 non-null object\n",
" 11 datetime 2393651 non-null object\n",
" 12 annotation_level 6569452 non-null object\n",
" 13 kingdom 2884628 non-null object\n",
" 14 phylum 2747716 non-null object\n",
" 15 subphylum 2747716 non-null object\n",
" 16 superclass 79 non-null object\n",
" 17 class 2720416 non-null object\n",
" 18 subclass 2419102 non-null object\n",
" 19 infraclass 2418989 non-null object\n",
" 20 superorder 2416407 non-null object\n",
" 21 order 2595808 non-null object\n",
" 22 suborder 1929192 non-null object\n",
" 23 infraorder 276567 non-null object\n",
" 24 superfamily 169159 non-null object\n",
" 25 family 2584024 non-null object\n",
" 26 subfamily 1851878 non-null object\n",
" 27 tribe 1637734 non-null object\n",
" 28 genus 2425063 non-null object\n",
" 29 species 2205055 non-null object\n",
" 30 subspecies 208933 non-null object\n",
" 31 variety 1480 non-null object\n",
"dtypes: int64(1), object(31)\n",
"memory usage: 1.6+ GB\n"
]
}
],
"source": [
"reduced_df.info(show_counts = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's remove empty frames to get a better sense of what we have."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 3539355 entries, 243177 to 23566051\n",
"Data columns (total 32 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 dataset_name 3539355 non-null object\n",
" 1 url_gcp 3539355 non-null object\n",
" 2 url_aws 3539355 non-null object\n",
" 3 url_azure 3539355 non-null object\n",
" 4 image_id 3539355 non-null object\n",
" 5 sequence_id 3539355 non-null object\n",
" 6 location_id 3539355 non-null object\n",
" 7 frame_num 3539355 non-null int64 \n",
" 8 original_label 3539355 non-null object\n",
" 9 scientific_name 2884628 non-null object\n",
" 10 common_name 2884628 non-null object\n",
" 11 datetime 2019552 non-null object\n",
" 12 annotation_level 3539355 non-null object\n",
" 13 kingdom 2884628 non-null object\n",
" 14 phylum 2747716 non-null object\n",
" 15 subphylum 2747716 non-null object\n",
" 16 superclass 79 non-null object\n",
" 17 class 2720416 non-null object\n",
" 18 subclass 2419102 non-null object\n",
" 19 infraclass 2418989 non-null object\n",
" 20 superorder 2416407 non-null object\n",
" 21 order 2595808 non-null object\n",
" 22 suborder 1929192 non-null object\n",
" 23 infraorder 276567 non-null object\n",
" 24 superfamily 169159 non-null object\n",
" 25 family 2584024 non-null object\n",
" 26 subfamily 1851878 non-null object\n",
" 27 tribe 1637734 non-null object\n",
" 28 genus 2425063 non-null object\n",
" 29 species 2205055 non-null object\n",
" 30 subspecies 208933 non-null object\n",
" 31 variety 1480 non-null object\n",
"dtypes: int64(1), object(31)\n",
"memory usage: 891.1+ MB\n"
]
}
],
"source": [
"df_cleaned = reduced_df.loc[reduced_df.original_label != \"empty\"].copy()\n",
"df_cleaned.info(show_counts = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not all have a scientific name, though those could be the non-taxa labels."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"original_label\n",
"problem 288579\n",
"blurred 184620\n",
"ignore 177546\n",
"unknown 2221\n",
"fire 1140\n",
"eye_shine 328\n",
"vehicle 293\n",
"Name: count, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[df_cleaned[\"scientific_name\"].isna(), \"original_label\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are clearly also labels to remove, so we can simply reduce down to only those with non-null `scientific_name` values as well."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 2884628 entries, 243177 to 23566051\n",
"Data columns (total 32 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 dataset_name 2884628 non-null object\n",
" 1 url_gcp 2884628 non-null object\n",
" 2 url_aws 2884628 non-null object\n",
" 3 url_azure 2884628 non-null object\n",
" 4 image_id 2884628 non-null object\n",
" 5 sequence_id 2884628 non-null object\n",
" 6 location_id 2884628 non-null object\n",
" 7 frame_num 2884628 non-null int64 \n",
" 8 original_label 2884628 non-null object\n",
" 9 scientific_name 2884628 non-null object\n",
" 10 common_name 2884628 non-null object\n",
" 11 datetime 1366269 non-null object\n",
" 12 annotation_level 2884628 non-null object\n",
" 13 kingdom 2884628 non-null object\n",
" 14 phylum 2747716 non-null object\n",
" 15 subphylum 2747716 non-null object\n",
" 16 superclass 79 non-null object\n",
" 17 class 2720416 non-null object\n",
" 18 subclass 2419102 non-null object\n",
" 19 infraclass 2418989 non-null object\n",
" 20 superorder 2416407 non-null object\n",
" 21 order 2595808 non-null object\n",
" 22 suborder 1929192 non-null object\n",
" 23 infraorder 276567 non-null object\n",
" 24 superfamily 169159 non-null object\n",
" 25 family 2584024 non-null object\n",
" 26 subfamily 1851878 non-null object\n",
" 27 tribe 1637734 non-null object\n",
" 28 genus 2425063 non-null object\n",
" 29 species 2205055 non-null object\n",
" 30 subspecies 208933 non-null object\n",
" 31 variety 1480 non-null object\n",
"dtypes: int64(1), object(31)\n",
"memory usage: 726.3+ MB\n"
]
}
],
"source": [
"df_cleaned = df_cleaned.loc[~df_cleaned[\"scientific_name\"].isna()].copy()\n",
"df_cleaned.info(show_counts=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dataset_name 7\n",
"url_gcp 2816041\n",
"url_aws 2816041\n",
"url_azure 2816041\n",
"image_id 2816041\n",
"sequence_id 933146\n",
"location_id 3090\n",
"frame_num 598\n",
"original_label 446\n",
"scientific_name 368\n",
"common_name 397\n",
"datetime 1216843\n",
"annotation_level 2\n",
"kingdom 1\n",
"phylum 2\n",
"subphylum 4\n",
"superclass 1\n",
"class 7\n",
"subclass 3\n",
"infraclass 2\n",
"superorder 5\n",
"order 44\n",
"suborder 14\n",
"infraorder 7\n",
"superfamily 8\n",
"family 101\n",
"subfamily 53\n",
"tribe 33\n",
"genus 234\n",
"species 283\n",
"subspecies 17\n",
"variety 1\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 368 unique `scientific_name` values, some of which were definitely just higher ranks (e.g., Aves), but there are 283 species, so somewhere between the two should be our biodiversity.\n",
"\n",
"Interesting also to note that there are duplicate URLs here; these would be the indicators of multiple species in an image as they correspond to the number of unique image IDs. Though, those could also be the by-sequence images that we expected to be by-image."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>suborder</th>\n",
" <th>infraorder</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [dataset_name, url_gcp, url_aws, url_azure, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety]\n",
"Index: []\n",
"\n",
"[0 rows x 32 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#double-check for humans\n",
"df_cleaned.loc[df_cleaned.species == \"homo sapien\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save the Reduced Data (no more \"empty\" labels)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"df_cleaned.to_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", index = False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"phylum\n",
"chordata 2740734\n",
"arthropoda 6982\n",
"Name: count, dtype: int64\n",
"\n",
"class\n",
"mammalia 2453739\n",
"aves 209290\n",
"reptilia 49813\n",
"insecta 6898\n",
"amphibia 592\n",
"malacostraca 79\n",
"arachnida 5\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"print(df_cleaned.phylum.value_counts())\n",
"print()\n",
"print(df_cleaned[\"class\"].value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All images are in Animalia, as expected; we have 2 phyla represented and 8 classes:\n",
" - Predominantly Chordata, and within that phylum, Mammalia is the vast majority, though aves is about 10%.\n",
" - Note that not every image with a phylum label has a class label.\n",
" - Insecta, malacostraca, and arachnida are all in the class Arthropoda.\n",
"\n",
"### Label Multi-Species Images\n",
"We'll go by both the URL and image ID, which do seem to correspond to the same images (for uniqueness)."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dataset_name 4\n",
"url_gcp 62877\n",
"url_aws 62877\n",
"url_azure 62877\n",
"image_id 62877\n",
"sequence_id 26489\n",
"location_id 944\n",
"frame_num 25\n",
"original_label 148\n",
"scientific_name 131\n",
"common_name 142\n",
"datetime 383\n",
"annotation_level 2\n",
"kingdom 1\n",
"phylum 2\n",
"subphylum 4\n",
"superclass 1\n",
"class 6\n",
"subclass 3\n",
"infraclass 2\n",
"superorder 4\n",
"order 25\n",
"suborder 9\n",
"infraorder 5\n",
"superfamily 4\n",
"family 45\n",
"subfamily 21\n",
"tribe 20\n",
"genus 86\n",
"species 95\n",
"subspecies 10\n",
"variety 1\n",
"multi_species 1\n",
"dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned[\"multi_species\"] = df_cleaned.duplicated(subset = [\"url_aws\", \"image_id\"], keep = False)\n",
"\n",
"df_cleaned.loc[df_cleaned[\"multi_species\"]].nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've got just under 63K images that have multiple species. We can figure out how many each of them have, and then move on to looking at images per sequence and other labeling info."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"multi_sp_imgs = list(df_cleaned.loc[df_cleaned[\"multi_species\"], \"image_id\"].unique())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" <th>multi_species</th>\n",
" <th>num_species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>243177</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>bird</td>\n",
" <td>aves</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243178</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 10</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>bird</td>\n",
" <td>aves</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243179</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 100</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern gray squirrel</td>\n",
" <td>sciurus carolinensis</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>sciurinae</td>\n",
" <td>sciurini</td>\n",
" <td>sciurus</td>\n",
" <td>sciurus carolinensis</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243180</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1000</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern chipmunk</td>\n",
" <td>tamias striatus</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>xerinae</td>\n",
" <td>tamiini</td>\n",
" <td>tamias</td>\n",
" <td>tamias striatus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243181</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 1001</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>eastern chipmunk</td>\n",
" <td>tamias striatus</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>sciuridae</td>\n",
" <td>xerinae</td>\n",
" <td>tamiini</td>\n",
" <td>tamias</td>\n",
" <td>tamias striatus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_name url_gcp \\\n",
"243177 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243178 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243179 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243180 ENA24 https://storage.googleapis.com/public-datasets... \n",
"243181 ENA24 https://storage.googleapis.com/public-datasets... \n",
"\n",
" url_aws \\\n",
"243177 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243178 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243179 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243180 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"243181 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"\n",
" url_azure image_id \\\n",
"243177 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1 \n",
"243178 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 10 \n",
"243179 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 100 \n",
"243180 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1000 \n",
"243181 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1001 \n",
"\n",
" sequence_id location_id frame_num original_label \\\n",
"243177 ENA24 : unknown ENA24 -1 bird \n",
"243178 ENA24 : unknown ENA24 -1 bird \n",
"243179 ENA24 : unknown ENA24 -1 eastern gray squirrel \n",
"243180 ENA24 : unknown ENA24 -1 eastern chipmunk \n",
"243181 ENA24 : unknown ENA24 -1 eastern chipmunk \n",
"\n",
" scientific_name ... superfamily family subfamily tribe \\\n",
"243177 aves ... NaN NaN NaN NaN \n",
"243178 aves ... NaN NaN NaN NaN \n",
"243179 sciurus carolinensis ... NaN sciuridae sciurinae sciurini \n",
"243180 tamias striatus ... NaN sciuridae xerinae tamiini \n",
"243181 tamias striatus ... NaN sciuridae xerinae tamiini \n",
"\n",
" genus species subspecies variety multi_species \\\n",
"243177 NaN NaN NaN NaN False \n",
"243178 NaN NaN NaN NaN False \n",
"243179 sciurus sciurus carolinensis NaN NaN False \n",
"243180 tamias tamias striatus NaN NaN False \n",
"243181 tamias tamias striatus NaN NaN False \n",
"\n",
" num_species \n",
"243177 NaN \n",
"243178 NaN \n",
"243179 NaN \n",
"243180 NaN \n",
"243181 NaN \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for img in multi_sp_imgs:\n",
" df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] = df_cleaned.loc[df_cleaned[\"image_id\"] == img].shape[0]\n",
" \n",
"df_cleaned.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set all the non-multi species images to show 1 in the `num_species` column."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"num_species\n",
"1.0 2753164\n",
"2.0 115436\n",
"3.0 14052\n",
"4.0 1704\n",
"5.0 230\n",
"14.0 42\n",
"Name: count, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[df_cleaned[\"num_species\"].isna(), \"num_species\"] = 1.0\n",
"\n",
"df_cleaned.num_species.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" <th>multi_species</th>\n",
" <th>num_species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20052440</th>\n",
" <td>Snapshot Safari 2024 Expansion</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Snapshot Safari 2024 Expansion : Snapshot Came...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1_EFA08</td>\n",
" <td>1</td>\n",
" <td>aardwolf</td>\n",
" <td>proteles cristatus</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>hyaenidae</td>\n",
" <td>protelinae</td>\n",
" <td>NaN</td>\n",
" <td>proteles</td>\n",
" <td>proteles cristatus</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20052425</th>\n",
" <td>Snapshot Safari 2024 Expansion</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Snapshot Safari 2024 Expansion : Snapshot Came...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1_EFA08</td>\n",
" <td>0</td>\n",
" <td>ostrich</td>\n",
" <td>struthionidae</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>struthionidae</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20052443</th>\n",
" <td>Snapshot Safari 2024 Expansion</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Snapshot Safari 2024 Expansion : Snapshot Came...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1_EFA08</td>\n",
" <td>1</td>\n",
" <td>lionmale</td>\n",
" <td>panthera leo</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>felidae</td>\n",
" <td>pantherinae</td>\n",
" <td>NaN</td>\n",
" <td>panthera</td>\n",
" <td>panthera leo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20052439</th>\n",
" <td>Snapshot Safari 2024 Expansion</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>Snapshot Safari 2024 Expansion : Snapshot Came...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...</td>\n",
" <td>Snapshot Safari 2024 Expansion : EFA_S1_EFA08</td>\n",
" <td>1</td>\n",
" <td>ostrich</td>\n",
" <td>struthionidae</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>struthionidae</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_name \\\n",
"20052440 Snapshot Safari 2024 Expansion \n",
"20052425 Snapshot Safari 2024 Expansion \n",
"20052443 Snapshot Safari 2024 Expansion \n",
"20052439 Snapshot Safari 2024 Expansion \n",
"\n",
" url_gcp \\\n",
"20052440 https://storage.googleapis.com/public-datasets... \n",
"20052425 https://storage.googleapis.com/public-datasets... \n",
"20052443 https://storage.googleapis.com/public-datasets... \n",
"20052439 https://storage.googleapis.com/public-datasets... \n",
"\n",
" url_aws \\\n",
"20052440 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"20052425 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"20052443 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"20052439 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"\n",
" url_azure \\\n",
"20052440 https://lilawildlife.blob.core.windows.net/lil... \n",
"20052425 https://lilawildlife.blob.core.windows.net/lil... \n",
"20052443 https://lilawildlife.blob.core.windows.net/lil... \n",
"20052439 https://lilawildlife.blob.core.windows.net/lil... \n",
"\n",
" image_id \\\n",
"20052440 Snapshot Safari 2024 Expansion : Snapshot Came... \n",
"20052425 Snapshot Safari 2024 Expansion : Snapshot Came... \n",
"20052443 Snapshot Safari 2024 Expansion : Snapshot Came... \n",
"20052439 Snapshot Safari 2024 Expansion : Snapshot Came... \n",
"\n",
" sequence_id \\\n",
"20052440 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n",
"20052425 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n",
"20052443 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n",
"20052439 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n",
"\n",
" location_id frame_num \\\n",
"20052440 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n",
"20052425 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 0 \n",
"20052443 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n",
"20052439 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n",
"\n",
" original_label scientific_name ... superfamily family \\\n",
"20052440 aardwolf proteles cristatus ... NaN hyaenidae \n",
"20052425 ostrich struthionidae ... NaN struthionidae \n",
"20052443 lionmale panthera leo ... NaN felidae \n",
"20052439 ostrich struthionidae ... NaN struthionidae \n",
"\n",
" subfamily tribe genus species subspecies variety \\\n",
"20052440 protelinae NaN proteles proteles cristatus NaN NaN \n",
"20052425 NaN NaN NaN NaN NaN NaN \n",
"20052443 pantherinae NaN panthera panthera leo NaN NaN \n",
"20052439 NaN NaN NaN NaN NaN NaN \n",
"\n",
" multi_species num_species \n",
"20052440 True 14.0 \n",
"20052425 True 14.0 \n",
"20052443 True 14.0 \n",
"20052439 True 14.0 \n",
"\n",
"[4 rows x 34 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[df_cleaned[\"num_species\"] == 14.0].sample(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Found a typo above with the human check... seems all taxa are lowercase, but let's make sure it's enough to catch them all"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"num homo sapiens: (16944, 34)\n"
]
},
{
"data": {
"text/plain": [
"(16944, 34)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"num homo sapiens: \", df_cleaned.loc[df_cleaned.species == \"homo sapiens\"].shape)\n",
"df_cleaned.loc[df_cleaned[\"original_label\"] == \"human\"].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Did any of these factor in to the multi-species counts?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(353, 34)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"])].shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>superfamily</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" <th>multi_species</th>\n",
" <th>num_species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>247895</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 4935</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>human</td>\n",
" <td>homo sapiens</td>\n",
" <td>...</td>\n",
" <td>hominoidea</td>\n",
" <td>hominidae</td>\n",
" <td>homininae</td>\n",
" <td>NaN</td>\n",
" <td>homo</td>\n",
" <td>homo sapiens</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247621</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 4804</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>human</td>\n",
" <td>homo sapiens</td>\n",
" <td>...</td>\n",
" <td>hominoidea</td>\n",
" <td>hominidae</td>\n",
" <td>homininae</td>\n",
" <td>NaN</td>\n",
" <td>homo</td>\n",
" <td>homo sapiens</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247631</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 4809</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>human</td>\n",
" <td>homo sapiens</td>\n",
" <td>...</td>\n",
" <td>hominoidea</td>\n",
" <td>hominidae</td>\n",
" <td>homininae</td>\n",
" <td>NaN</td>\n",
" <td>homo</td>\n",
" <td>homo sapiens</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250361</th>\n",
" <td>ENA24</td>\n",
" <td>https://storage.googleapis.com/public-datasets...</td>\n",
" <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
" <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
" <td>ENA24 : 6989</td>\n",
" <td>ENA24 : unknown</td>\n",
" <td>ENA24</td>\n",
" <td>-1</td>\n",
" <td>human</td>\n",
" <td>homo sapiens</td>\n",
" <td>...</td>\n",
" <td>hominoidea</td>\n",
" <td>hominidae</td>\n",
" <td>homininae</td>\n",
" <td>NaN</td>\n",
" <td>homo</td>\n",
" <td>homo sapiens</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_name url_gcp \\\n",
"247895 ENA24 https://storage.googleapis.com/public-datasets... \n",
"247621 ENA24 https://storage.googleapis.com/public-datasets... \n",
"247631 ENA24 https://storage.googleapis.com/public-datasets... \n",
"250361 ENA24 https://storage.googleapis.com/public-datasets... \n",
"\n",
" url_aws \\\n",
"247895 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"247621 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"247631 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"250361 http://us-west-2.opendata.source.coop.s3.amazo... \n",
"\n",
" url_azure image_id \\\n",
"247895 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4935 \n",
"247621 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4804 \n",
"247631 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4809 \n",
"250361 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 6989 \n",
"\n",
" sequence_id location_id frame_num original_label scientific_name \\\n",
"247895 ENA24 : unknown ENA24 -1 human homo sapiens \n",
"247621 ENA24 : unknown ENA24 -1 human homo sapiens \n",
"247631 ENA24 : unknown ENA24 -1 human homo sapiens \n",
"250361 ENA24 : unknown ENA24 -1 human homo sapiens \n",
"\n",
" ... superfamily family subfamily tribe genus species \\\n",
"247895 ... hominoidea hominidae homininae NaN homo homo sapiens \n",
"247621 ... hominoidea hominidae homininae NaN homo homo sapiens \n",
"247631 ... hominoidea hominidae homininae NaN homo homo sapiens \n",
"250361 ... hominoidea hominidae homininae NaN homo homo sapiens \n",
"\n",
" subspecies variety multi_species num_species \n",
"247895 NaN NaN True 2.0 \n",
"247621 NaN NaN True 2.0 \n",
"247631 NaN NaN True 2.0 \n",
"250361 NaN NaN True 2.0 \n",
"\n",
"[4 rows x 34 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"])].sample(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's fix those counts then."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"num_species\n",
"1.0 2753832\n",
"2.0 114825\n",
"3.0 13995\n",
"4.0 1704\n",
"5.0 230\n",
"14.0 42\n",
"Name: count, dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"human_multi_species = list(df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"]), \"image_id\"].unique())\n",
"\n",
"for img in human_multi_species:\n",
" df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] = df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] - 1\n",
"\n",
"df_cleaned.num_species.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Actually remove human indicators"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"df_cleaned = df_cleaned.loc[df_cleaned[\"species\"] != \"homo sapiens\"].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Need to remove the images that have humans and other species too."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"df_cleaned = df_cleaned.loc[~df_cleaned[\"image_id\"].isin(human_multi_species)].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Save this to CSV now we got those counts"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"df_cleaned.to_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate individual CSVs for the datasets"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"for dataset in datasets_of_interest:\n",
" df_cleaned.loc[df_cleaned[\"dataset_name\"] == dataset].to_csv(dataset+\"_image_urls_and_labels.csv\", index = False)\n",
"\n",
"# Manually moved these to the data/potential-test-sets/ directory and renamed to not have spaces in the filenames\n",
"# (replaced spaces with underscores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get some basic stats"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"there are 2867312 images\n",
"we have 367 unique scientific names\n",
"when we filter for image-level labels, we have 184 scientific names\n"
]
}
],
"source": [
"print(f\"there are {df_cleaned.shape[0]} images\")\n",
"print(f\"we have {df_cleaned['scientific_name'].nunique()} unique scientific names\")\n",
"print(f\"when we filter for image-level labels, we have {df_cleaned.loc[df_cleaned['annotation_level'] == 'image', 'scientific_name'].nunique()} scientific names\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"num_species\n",
"1.0 305821\n",
"2.0 1154\n",
"3.0 3\n",
"Name: count, dtype: int64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[df_cleaned['annotation_level'] == 'image', 'num_species'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will want to dedicate some more time to exploring some of these taxonomic counts, but we'll first look at the number of unique taxa (by Linnean 7-rank (`unique_7_tuple`)). We'll compare these to the number of unique scientific and common names, then perhaps add a count of number of creatures based on one of those labels. At that point we may save another copy of this CSV and start a new analysis notebook."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"annotation_level\n",
"sequence 2560334\n",
"image 306978\n",
"Name: count, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.annotation_level.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get a sense of total number of unique taxa, then separate out the by-image ones for unique taxa count there. Then we'll separate out each dataset into its own CSV for individual analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Taxonomic String Exploration"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"lin_taxa = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### How many have all 7 Linnean ranks?"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 2187756 entries, 243179 to 23565561\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 kingdom 2187756 non-null object\n",
" 1 phylum 2187756 non-null object\n",
" 2 class 2187756 non-null object\n",
" 3 order 2187756 non-null object\n",
" 4 family 2187756 non-null object\n",
" 5 genus 2187756 non-null object\n",
" 6 species 2187756 non-null object\n",
"dtypes: object(7)\n",
"memory usage: 133.5+ MB\n"
]
}
],
"source": [
"df_all_taxa = df_cleaned.dropna(subset = lin_taxa)\n",
"df_all_taxa[lin_taxa].info(show_counts = True)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 249847 entries, 243179 to 19469709\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 kingdom 249847 non-null object\n",
" 1 phylum 249847 non-null object\n",
" 2 class 249847 non-null object\n",
" 3 order 249847 non-null object\n",
" 4 family 249847 non-null object\n",
" 5 genus 249847 non-null object\n",
" 6 species 249847 non-null object\n",
"dtypes: object(7)\n",
"memory usage: 15.2+ MB\n"
]
}
],
"source": [
"df_all_taxa_img = df_cleaned.loc[df_cleaned[\"annotation_level\"] == \"image\"].dropna(subset = lin_taxa)\n",
"df_all_taxa_img[lin_taxa].info(show_counts = True)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(306978, 34)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.loc[df_cleaned[\"annotation_level\"] == \"image\"].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's not too bad, considering some are definitely just common names or classes: 2,187,756 out of 2,867,312. \n",
"\n",
"249,847 when we drop to just image-level annotations (out of 306,978).\n",
"\n",
"\n",
"Now how many different 7-tuples are there?\n",
"\n",
"#### How many unique 7-tuples?"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"unique taxa in all: 355\n",
"unique taxa in image-level labeled: 180\n"
]
}
],
"source": [
"#number of unique 7-tuples in full dataset\n",
"df_cleaned['lin_duplicate'] = df_cleaned.duplicated(subset = lin_taxa, keep = 'first')\n",
"df_unique_lin_taxa = df_cleaned.loc[~df_cleaned['lin_duplicate']].copy()\n",
"print(f\"unique taxa in all: {df_unique_lin_taxa.shape[0]}\")\n",
"print(f\"unique taxa in image-level labeled: {df_unique_lin_taxa.loc[df_unique_lin_taxa[\"annotation_level\"] == \"image\"].shape[0]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretty much aligns with the scientific name counts."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"355"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_unique_lin_taxa.scientific_name.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_name</th>\n",
" <th>url_gcp</th>\n",
" <th>url_aws</th>\n",
" <th>url_azure</th>\n",
" <th>image_id</th>\n",
" <th>sequence_id</th>\n",
" <th>location_id</th>\n",
" <th>frame_num</th>\n",
" <th>original_label</th>\n",
" <th>scientific_name</th>\n",
" <th>...</th>\n",
" <th>family</th>\n",
" <th>subfamily</th>\n",
" <th>tribe</th>\n",
" <th>genus</th>\n",
" <th>species</th>\n",
" <th>subspecies</th>\n",
" <th>variety</th>\n",
" <th>multi_species</th>\n",
" <th>num_species</th>\n",
" <th>lin_duplicate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [dataset_name, url_gcp, url_aws, url_azure, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety, multi_species, num_species, lin_duplicate]\n",
"Index: []\n",
"\n",
"[0 rows x 35 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_unique_lin_taxa.loc[(df_unique_lin_taxa[\"scientific_name\"].isna()) | (df_unique_lin_taxa[\"common_name\"].isna())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check out our top ten labels, scientific names, and common names. Then we'll save this cleaned metadata file."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"original_label\n",
"eurasian_wild_pig 234736\n",
"impala 231338\n",
"zebraplains 121619\n",
"wildebeestblue 121208\n",
"large_antlered_muntjac 119774\n",
"elephant 119579\n",
"unknown 117522\n",
"unidentified_murid 98858\n",
"sambar 74459\n",
"stump_tailed_macaque 73543\n",
"Name: count, dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned[\"original_label\"].value_counts()[:10]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"scientific_name\n",
"sus scrofa 237022\n",
"aepyceros melampus 231338\n",
"animalia 136912\n",
"equus quagga 121619\n",
"connochaetes taurinus taurinus 121208\n",
"loxodonta africana 121013\n",
"muntiacus vuquangensis 119774\n",
"muridae 98858\n",
"aves 80435\n",
"rusa unicolor 74459\n",
"Name: count, dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned[\"scientific_name\"].value_counts()[:10]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"common_name\n",
"eurasian wild pig 234736\n",
"impala 231338\n",
"plains zebra 121619\n",
"blue wildebeest 121208\n",
"large-antlered muntjac 119774\n",
"african bush elephant 119579\n",
"unkown animal 117522\n",
"old-world mice and rats 98858\n",
"bird 79568\n",
"sambar 74459\n",
"Name: count, dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned[\"common_name\"].value_counts()[:10]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Count', ylabel='class'>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df_cleaned, y = 'class')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Count', ylabel='order'>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df_cleaned.loc[df_cleaned[\"class\"].isin([\"aves\", \"mammalia\", \"reptilia\"])], y = 'order')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,py:percent"
},
"kernelspec": {
"display_name": "data-dev",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|