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
File size: 406 Bytes
9241534 d892f1d 9241534 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"epoch": 9.995635093845483,
"eval_accuracy": 0.9777191259513872,
"eval_loss": 0.07020638883113861,
"eval_runtime": 72.0557,
"eval_samples_per_second": 226.103,
"eval_steps_per_second": 7.078,
"total_flos": 3.92899376185344e+18,
"train_loss": 0.3148357209992721,
"train_runtime": 13104.719,
"train_samples_per_second": 111.886,
"train_steps_per_second": 0.874
} |