Image Classification
Transformers
Safetensors
timm
timm_wrapper
vision
trackio
Generated from Trainer
Instructions to use evalstate/ic-smoke-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evalstate/ic-smoke-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="evalstate/ic-smoke-test") 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("evalstate/ic-smoke-test") model = AutoModelForImageClassification.from_pretrained("evalstate/ic-smoke-test") - timm
How to use evalstate/ic-smoke-test with timm:
import timm model = timm.create_model("hf_hub:evalstate/ic-smoke-test", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 2.0, | |
| "test_accuracy": 0.453125, | |
| "test_loss": 1.0237222909927368, | |
| "test_runtime": 0.7792, | |
| "test_samples_per_second": 164.261, | |
| "test_steps_per_second": 10.266, | |
| "total_flos": 274731241881600.0, | |
| "train_loss": 1.033449445452009, | |
| "train_runtime": 3.106, | |
| "train_samples_per_second": 64.392, | |
| "train_steps_per_second": 4.507 | |
| } |