Instructions to use adarshcod30/openforensics-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use adarshcod30/openforensics-ensemble with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://adarshcod30/openforensics-ensemble") - Notebooks
- Google Colab
- Kaggle
| epoch,accuracy,auc,learning_rate,loss,pr_auc,precision,recall,val_accuracy,val_auc,val_loss,val_pr_auc,val_precision,val_recall | |
| 0,0.8314499855041504,0.9154987335205078,9.999999747378752e-06,0.36911219358444214,0.9143701791763306,0.8218898773193359,0.8463000059127808,0.9171666502952576,0.9739356637001038,0.21560244262218475,0.974362850189209,0.9246691465377808,0.9083333611488342 | |
| 1,0.9017500281333923,0.9668275713920593,9.999999747378752e-06,0.24103708565235138,0.9671510457992554,0.8959298133850098,0.9090999960899353,0.9254999756813049,0.9824355244636536,0.18814511597156525,0.9829871654510498,0.9020472168922424,0.9546666741371155 | |
| 2,0.92044997215271,0.9775867462158203,9.999999747378752e-06,0.19698910415172577,0.9771064519882202,0.9190670847892761,0.9221000075340271,0.9448333382606506,0.9881657361984253,0.1481364518404007,0.9885095357894897,0.9429140686988831,0.9470000267028809 | |
| 3,0.9316999912261963,0.9827858209609985,9.999999747378752e-06,0.17176666855812073,0.9824005961418152,0.9316999912261963,0.9316999912261963,0.9495000243186951,0.9910013675689697,0.1246447041630745,0.9912596940994263,0.9496498703956604,0.9493333101272583 | |
| 4,0.9442999958992004,0.9861769676208496,9.999999747378752e-06,0.15096934139728546,0.9856390357017517,0.9425298571586609,0.9463000297546387,0.9601666927337646,0.9931068420410156,0.10863322764635086,0.993274450302124,0.9492352604866028,0.9723333120346069 | |
| 5,0.9483000040054321,0.9886840581893921,9.999999747378752e-06,0.13672402501106262,0.9884840250015259,0.9465139508247375,0.9502999782562256,0.9564999938011169,0.9929454922676086,0.11299299448728561,0.9920790195465088,0.9362854361534119,0.9796666502952576 | |
| 6,0.9489499926567078,0.9896972179412842,9.999999747378752e-06,0.12901079654693604,0.989821195602417,0.9496244192123413,0.948199987411499,0.9580000042915344,0.9920089244842529,0.11542414128780365,0.9923437237739563,0.9648173451423645,0.9506666660308838 | |
| 7,0.955049991607666,0.9918136596679688,9.999999747378752e-06,0.11561071872711182,0.9916909337043762,0.9532821774482727,0.9570000171661377,0.9664999842643738,0.9961512088775635,0.09274118393659592,0.9965484142303467,0.9484139680862427,0.9866666793823242 | |
| 8,0.9573500156402588,0.9923415780067444,9.999999747378752e-06,0.11129580438137054,0.991878092288971,0.9555732607841492,0.9592999815940857,0.9621666669845581,0.9936205744743347,0.1011066809296608,0.9937077760696411,0.9676222801208496,0.956333339214325 | |
| 9,0.9640499949455261,0.9939858913421631,9.999999747378752e-06,0.09820930659770966,0.9941444396972656,0.9635400772094727,0.9646000266075134,0.9635000228881836,0.9956977367401123,0.09978923201560974,0.9955255389213562,0.9432578682899475,0.9863333106040955 | |