Image Classification
Transformers
PyTorch
TensorBoard
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use douwekiela/resnet-18-finetuned-dogfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use douwekiela/resnet-18-finetuned-dogfood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="douwekiela/resnet-18-finetuned-dogfood") 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("douwekiela/resnet-18-finetuned-dogfood") model = AutoModelForImageClassification.from_pretrained("douwekiela/resnet-18-finetuned-dogfood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "global_step": 16, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.62, | |
| "learning_rate": 2.1428571428571428e-05, | |
| "loss": 0.846, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.9155555555555556, | |
| "eval_loss": 0.26624444127082825, | |
| "eval_runtime": 2.4885, | |
| "eval_samples_per_second": 90.415, | |
| "eval_steps_per_second": 3.215, | |
| "step": 16 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "step": 16, | |
| "total_flos": 2.04437073802752e+16, | |
| "train_loss": 0.6555470079183578, | |
| "train_runtime": 34.238, | |
| "train_samples_per_second": 59.145, | |
| "train_steps_per_second": 0.467 | |
| } | |
| ], | |
| "max_steps": 16, | |
| "num_train_epochs": 1, | |
| "total_flos": 2.04437073802752e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |