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": 19.965576592082616, | |
| "eval_accuracy": 0.9781976744186046, | |
| "eval_loss": 0.08412329852581024, | |
| "eval_runtime": 12.08, | |
| "eval_samples_per_second": 170.861, | |
| "eval_steps_per_second": 5.381, | |
| "total_flos": 9.813099581032366e+17, | |
| "train_loss": 0.3346556580683281, | |
| "train_runtime": 3422.7405, | |
| "train_samples_per_second": 108.533, | |
| "train_steps_per_second": 0.847 | |
| } |