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
| { | |
| "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 | |
| } |