Zero-Shot Image Classification
Transformers
Safetensors
English
clip
fashion
multimodal
image-search
text-search
embeddings
contrastive-learning
zero-shot-classification
Instructions to use Leacb4/gap-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leacb4/gap-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Leacb4/gap-clip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Leacb4/gap-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("Leacb4/gap-clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload models/gap_clip.pth with huggingface_hub
Browse files- models/gap_clip.pth +2 -2
models/gap_clip.pth
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:f3ca87d49bd97a3e9a0bb7638d181c0e0adc965b4d937d39c64ee3793823ba3c
|
| 3 |
+
size 1815802589
|