Instructions to use GimhanSathsara843/cataract-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use GimhanSathsara843/cataract-classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://GimhanSathsara843/cataract-classifier") - Notebooks
- Google Colab
- Kaggle
| epoch,acc,auc,learning_rate,loss,val_acc,val_auc,val_loss,val_qwk | |
| 0,0.9697352051734924,0.9969000816345215,9.999999747378752e-06,0.3061612844467163,0.9571428298950195,0.9834694266319275,0.5446611046791077,0.9649122807017544 | |
| 1,0.9770290851593018,0.9983141422271729,9.999999747378752e-06,0.29130321741104126,0.9571428298950195,0.984183669090271,0.5117390155792236,0.9649122807017544 | |
| 2,0.9770290851593018,0.9989427328109741,9.999999747378752e-06,0.29007160663604736,0.9571428298950195,0.9847959280014038,0.4835590422153473,0.9649122807017544 | |
| 3,0.9816232919692993,0.9996505975723267,9.999999747378752e-06,0.2772858440876007,0.9571428298950195,0.9851020574569702,0.46022725105285645,0.9649122807017544 | |
| 4,0.9862174391746521,0.9992077946662903,9.999999747378752e-06,0.2821372151374817,0.9571428298950195,0.9848979711532593,0.43698522448539734,0.9649122807017544 | |
| 5,0.9785605072975159,0.9994183778762817,9.999999747378752e-06,0.2800840735435486,0.9571428298950195,0.9851020574569702,0.41646909713745117,0.9649122807017544 | |
| 6,0.9846860766410828,0.999528169631958,9.999999747378752e-06,0.2756785452365875,0.9571428298950195,0.9852041006088257,0.39905622601509094,0.9649122807017544 | |
| 7,0.9846860766410828,0.9986217021942139,9.999999747378752e-06,0.2782074511051178,0.9642857313156128,0.9847279191017151,0.38796377182006836,0.9767441860465116 | |
| 8,0.9816232919692993,0.9990050792694092,9.999999747378752e-06,0.282250314950943,0.9642857313156128,0.9858503937721252,0.38022613525390625,0.9767441860465116 | |
| 9,0.9754977226257324,0.9990837574005127,9.999999747378752e-06,0.286131888628006,0.9642857313156128,0.9854421615600586,0.3682955503463745,0.9767441860465116 | |
| 10,0.972434937953949,0.9988800287246704,9.999999747378752e-06,0.2914201319217682,0.9642857313156128,0.9858503341674805,0.36018210649490356,0.9767441860465116 | |
| 11,0.9831546545028687,0.9990031123161316,9.999999747378752e-06,0.28271886706352234,0.9571428298950195,0.9856122136116028,0.3562229871749878,0.9738372093023255 | |
| 12,0.9862174391746521,0.9994187355041504,9.999999747378752e-06,0.27243199944496155,0.9571428298950195,0.9855782389640808,0.355712890625,0.9738372093023255 | |
| 13,0.9754977226257324,0.9987905025482178,9.999999747378752e-06,0.2867102026939392,0.9571428298950195,0.9854081273078918,0.3558560311794281,0.9738372093023255 | |
| 14,0.9816232919692993,0.9986081719398499,9.999999747378752e-06,0.28760719299316406,0.9571428298950195,0.9851020574569702,0.35514116287231445,0.9738372093023255 | |
| 15,0.9877488613128662,0.9993962049484253,9.999999747378752e-06,0.27327069640159607,0.9642857313156128,0.9864625930786133,0.35424885153770447,0.9767441860465116 | |