Instructions to use GimhanSathsara843/cataract-severity-efficientnetb0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use GimhanSathsara843/cataract-severity-efficientnetb0 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-severity-efficientnetb0") - Notebooks
- Google Colab
- Kaggle
| epoch,acc,learning_rate,loss,val_acc,val_loss,val_qwk | |
| 0,0.6612377762794495,0.0010000000474974513,1.0813597440719604,0.4237288236618042,1.1277486085891724,0.2985772753849152 | |
| 1,0.8973941206932068,0.0010000000474974513,0.6030792593955994,0.7457627058029175,0.8226550221443176,0.6426790167438546 | |
| 2,0.9348534345626831,0.0010000000474974513,0.504050612449646,0.8644067645072937,0.6954477429389954,0.719047619047619 | |
| 3,0.9446253776550293,0.0010000000474974513,0.39159250259399414,0.8813559412956238,0.6843019723892212,0.7285457091418284 | |
| 4,0.9495114088058472,0.0010000000474974513,0.4188724458217621,0.8644067645072937,0.7304669618606567,0.723545490042952 | |
| 5,0.9511400461196899,0.0010000000474974513,0.36688876152038574,0.8135592937469482,0.692980945110321,0.7009573868969401 | |
| 6,0.9723126888275146,0.0010000000474974513,0.3414730727672577,0.8813559412956238,0.6467596888542175,0.7329265892540838 | |
| 7,0.9706840515136719,0.0010000000474974513,0.335051566362381,0.9152542352676392,0.6519331932067871,0.7578659370725034 | |
| 8,0.9706840515136719,0.0010000000474974513,0.32793745398521423,0.9322034120559692,0.5852716565132141,0.7675334909377463 | |
| 9,0.9576547145843506,0.0010000000474974513,0.35312023758888245,0.9152542352676392,0.5942530035972595,0.7578659370725034 | |
| 10,0.9739413857460022,0.0010000000474974513,0.327210396528244,0.9152542352676392,0.534002959728241,0.763323782234957 | |
| 11,0.9641693830490112,0.0010000000474974513,0.32755348086357117,0.9152542352676392,0.5403637886047363,0.8501074848544069 | |
| 12,0.9804560542106628,0.0010000000474974513,0.3122323453426361,0.9322034120559692,0.47280600666999817,0.7675334909377463 | |
| 13,0.975570023059845,0.0010000000474974513,0.30920541286468506,0.8983050584793091,0.48284080624580383,0.7723765432098766 | |
| 14,0.9706840515136719,0.0010000000474974513,0.32432639598846436,0.8813559412956238,0.5428078770637512,0.7052267486548809 | |
| 15,0.9739413857460022,0.0010000000474974513,0.32023704051971436,0.9152542352676392,0.4765341877937317,0.8501074848544069 | |
| 16,0.9739413857460022,0.0010000000474974513,0.3043287396430969,0.8983050584793091,0.5218567848205566,0.9302600472813238 | |
| 17,0.9804560542106628,0.0010000000474974513,0.30412283539772034,0.8983050584793091,0.43766137957572937,0.7483520744474603 | |
| 18,0.975570023059845,0.0010000000474974513,0.29872557520866394,0.9322034120559692,0.4098648130893707,0.9131075110456554 | |
| 19,0.9853420257568359,0.0010000000474974513,0.2886912226676941,0.8983050584793091,0.43425893783569336,0.7522900763358779 | |
| 20,0.9804560542106628,0.0010000000474974513,0.2837938964366913,0.9491525292396545,0.3998212516307831,0.9627603618767094 | |
| 21,0.9788273572921753,0.0010000000474974513,0.2839686870574951,0.9152542352676392,0.42340466380119324,0.809215844785772 | |
| 22,0.9885993599891663,0.0010000000474974513,0.2704813480377197,0.9322034120559692,0.3931242823600769,0.7712291585886002 | |
| 23,0.9837133288383484,0.0010000000474974513,0.27949148416519165,0.9661017060279846,0.37841087579727173,0.881858229875851 | |
| 24,0.9869706630706787,0.0010000000474974513,0.2755202353000641,0.9661017060279846,0.37384745478630066,0.881858229875851 | |