Image Classification
Transformers
TensorBoard
Safetensors
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Instructions to use A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2") 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("A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2") model = AutoModelForImageClassification.from_pretrained("A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 434 Bytes
e222397 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"crop_size": {
"height": 224,
"width": 224
},
"do_center_crop": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "MobileNetV2ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 256
},
"use_square_size": false
}
|