Image Classification
Transformers
TensorBoard
Safetensors
vit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use amunchet/rorshark-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amunchet/rorshark-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amunchet/rorshark-vit-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("amunchet/rorshark-vit-base") model = AutoModelForImageClassification.from_pretrained("amunchet/rorshark-vit-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download eval_results.json from amunchet/rorshark-vit-base: direct link, hf CLI and curl.
- Browser
- Download file 203 Bytes
-
https://huggingface.co/amunchet/rorshark-vit-base/resolve/main/eval_results.json
- Command line
-
hf download hf://amunchet/rorshark-vit-base/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/amunchet/rorshark-vit-base/resolve/main/eval_results.json
203 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9922928709055877, | |
| "eval_loss": 0.03933868557214737, | |
| "eval_runtime": 6.4711, | |
| "eval_samples_per_second": 80.203, | |
| "eval_steps_per_second": 10.045 | |
| } |