Instructions to use xprotocol/EfficientNet-B3-Cattle-Disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use xprotocol/EfficientNet-B3-Cattle-Disease with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://xprotocol/EfficientNet-B3-Cattle-Disease") - Notebooks
- Google Colab
- Kaggle
| epoch,accuracy,auc,loss,precision,recall,val_accuracy,val_auc,val_loss,val_precision,val_recall | |
| 0,0.9015282988548279,0.9813686013221741,0.023520061746239662,0.9067256450653076,0.8897186517715454,0.9481361508369446,0.9917148351669312,0.013013717718422413,0.9499589800834656,0.9384116530418396 | |
| 1,0.9155957102775574,0.9844353199005127,0.021358519792556763,0.9229545593261719,0.9070857763290405,0.9554294943809509,0.9933496713638306,0.01165955513715744,0.9581966996192932,0.9473257660865784 | |
| 2,0.9147273302078247,0.9851114749908447,0.020956283435225487,0.9217483401298523,0.9083014726638794,0.9578605890274048,0.9937500953674316,0.011287463828921318,0.9614121317863464,0.9489465355873108 | |
| 3,0.9256686568260193,0.9877729415893555,0.018358465284109116,0.9306825995445251,0.9187217950820923,0.9578605890274048,0.9937552809715271,0.0112152099609375,0.9614754319190979,0.9505672454833984 | |
| 4,0.9218478798866272,0.9876391887664795,0.018309835344552994,0.9266103506088257,0.9143800139427185,0.9562398791313171,0.9936745166778564,0.011330422013998032,0.9621710777282715,0.9481361508369446 | |
| 5,0.9230635762214661,0.9882681965827942,0.01801360957324505,0.9283192157745361,0.9131643176078796,0.9546191096305847,0.9939209222793579,0.011200509034097195,0.9598689675331116,0.9497568607330322 | |
| 6,0.9244529604911804,0.9882096648216248,0.018241871148347855,0.9293248653411865,0.9180271029472351,0.9570502638816833,0.9943813681602478,0.010834911838173866,0.9632353186607361,0.9554294943809509 | |
| 7,0.9263633489608765,0.9896754622459412,0.016594722867012024,0.9302244186401367,0.9215005040168762,0.9635332226753235,0.9945738315582275,0.010448659770190716,0.9681112170219421,0.9594813585281372 | |
| 8,0.9334838390350342,0.9909735918045044,0.015133296139538288,0.9385779500007629,0.926189661026001,0.968395471572876,0.9947263598442078,0.010319088585674763,0.970588207244873,0.9627228379249573 | |
| 9,0.9360889196395874,0.9911639094352722,0.015064474195241928,0.9391441345214844,0.9300104379653931,0.9659643173217773,0.9945825934410095,0.01037087943404913,0.9673735499382019,0.9611021280288696 | |
| 10,0.9357416033744812,0.9914368391036987,0.014717222191393375,0.9405784606933594,0.9319208264350891,0.9635332226753235,0.9944073557853699,0.010584930889308453,0.9648118019104004,0.9554294943809509 | |
| 11,0.9343522191047668,0.9915770888328552,0.014476170763373375,0.9378392696380615,0.9301840662956238,0.9627228379249573,0.9945300221443176,0.01062203012406826,0.9663934707641602,0.9554294943809509 | |
| 12,0.941993772983551,0.993187427520752,0.012407258152961731,0.9459744095802307,0.9366099238395691,0.9602917432785034,0.9943435192108154,0.010793819092214108,0.9647541046142578,0.9538087248802185 | |
| 13,0.9428620934486389,0.9929303526878357,0.0127909816801548,0.9469617009162903,0.9364362359046936,0.9619124531745911,0.9944791197776794,0.010668933391571045,0.9663658738136292,0.9546191096305847 | |
| 14,0.9357416033744812,0.9916419386863708,0.014414060860872269,0.9401229023933411,0.9298367500305176,0.9602917432785034,0.9946197867393494,0.01051716972142458,0.9647541046142578,0.9538087248802185 | |
| 15,0.9426884055137634,0.9933369159698486,0.012643888592720032,0.9487719535827637,0.9392150044441223,0.9570502638816833,0.9943536520004272,0.010901479981839657,0.9615069627761841,0.9513776302337646 | |