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
- Xet hash:
- b1cd6660e75eaa39c796d5b8b3891ff0ff62f464d11e7dbd75a1a5005f61a5bd
- Size of remote file:
- 5.33 kB
- SHA256:
- 99e019474217bd8f6d365c6a402d0c1894dd278aa90b944e298a801a48c66957
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.