Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/food") 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("nateraw/food") model = AutoModelForImageClassification.from_pretrained("nateraw/food", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
add weights
Browse files- .gitattributes +1 -0
- pytorch_model.bin +3 -0
.gitattributes
CHANGED
|
@@ -15,3 +15,4 @@
|
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 17 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 15 |
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 16 |
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 17 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:33d6b7d44c0bb524fbb6f6d3b7d1f7c8d7c703b58882bf2a845355d9411f566d
|
| 3 |
+
size 343584369
|