Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use daliapv/vit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daliapv/vit-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="daliapv/vit-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("daliapv/vit-model") model = AutoModelForImageClassification.from_pretrained("daliapv/vit-model") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
runs/Sep15_17-55-42_b4a398ca327c/events.out.tfevents.1694800615.b4a398ca327c.3413.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8d710df214f5b52816c3ca5e161765b3e0cdcefe9799c0c873f28d97d3944d0d
|
| 3 |
+
size 4917
|