Instructions to use sdmlai/plantdoc-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sdmlai/plantdoc-predictor with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://sdmlai/plantdoc-predictor") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
This model has 4 files scanned as suspicious.
- alexnet_v1_plant_disease
- convnext_base_v1_plant_disease
- convnext_small_v1_plant_disease
- convnext_tiny_v1_plant_disease
- deit_base_v1
- deit_small_v1
- densenet121_v1_plant_disease
- densenet169_v1_plant_disease
- densenet210_v1_plant_disease
- efficientnetb50_v1
- inceptionv3_v1
- mobilenetv2_v1
- recursive_additive_attention_v1_plant_disease
- regnetx160plantvillagev1
- regnety160plantvillagev1
- regnety320plantvillage_v1
- resnet50_v1
- swin_base_patch4_windows7
- swin_tiny_patch4_windows7
- vgg16_v1_plant_disease
- vgg19_v1_plant_disease
- vit_base_v1
- vit_large_v1
- vit_small_v1
- vit_tiny_v1