Image Classification
Transformers
Safetensors
swinv2
medical-imaging
thyroid
ultrasound
Generated from Trainer
ml-intern
Eval Results (legacy)
Instructions to use Johnyquest7/TN5000_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Johnyquest7/TN5000_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Johnyquest7/TN5000_model") 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("Johnyquest7/TN5000_model") model = AutoModelForImageClassification.from_pretrained("Johnyquest7/TN5000_model") - Notebooks
- Google Colab
- Kaggle
File size: 337 Bytes
94a12ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "ViTImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 256,
"width": 256
}
}
|