Image Classification
Transformers
TensorBoard
Safetensors
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Instructions to use ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease") model = AutoModelForImageClassification.from_pretrained("ozair23/mobilenet_v2_1.0_224-finetuned-plantdisease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d3514c19c4753d6cbecc997f7d9b0c90c3742f0f11a9a6b2170b83f6ff1fbf7
- Size of remote file:
- 9.15 MB
- SHA256:
- 941cc4f8631c592192a33858cc3d2d98df63d3aef8eeb7a3159364074a78b3a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.