Image Classification
Transformers
Safetensors
siglip_vision_model
Generated from Trainer
siglip
custom_code
Instructions to use p1atdev/siglip-tagger-test-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/siglip-tagger-test-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="p1atdev/siglip-tagger-test-3", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Upload bf16 model
Browse files- model.safetensors +2 -2
model.safetensors
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:ce46ef29aec79fcf0fbe8280521acb10381ef5000706af5f870c08af781fb3eb
|
| 3 |
+
size 878455682
|