Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-Training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-Training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-Training") 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("DeepLearner101/ImageNetSelectedSubsetBasedModel-Training") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-Training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
a6147a4
1
Parent(s): 2329f0c
Add pytorch_model_0154.pth and related files
Browse files- pytorch_model_0154.pth +3 -0
- training_metrics_0154.json +0 -0
pytorch_model_0154.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1bb9b456893549cd2668305c3258146de0defaefc173289b23d7c50d9080c05f
|
| 3 |
+
size 94434839
|
training_metrics_0154.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|