Instructions to use Arm627/WaspImageRecComap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arm627/WaspImageRecComap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Arm627/WaspImageRecComap") 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("Arm627/WaspImageRecComap") model = AutoModelForImageClassification.from_pretrained("Arm627/WaspImageRecComap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 741 Bytes
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tags:
- autotrain
- vision
- image-classification
datasets:
- Arm627/autotrain-data-wasp_classification_2.0
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.8068778967193102
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2977085925
- CO2 Emissions (in grams): 0.8069
## Validation Metrics
- Loss: 0.647
- Accuracy: 0.697
- Precision: 0.065
- Recall: 0.667
- AUC: 0.601
- F1: 0.118 |