Zero-Shot Image Classification
Transformers
ONNX
clip
robotics
edge-deployment
anima
forge
int8
quantized
vision
zero-shot
image-classification
ros2
jetson
real-time
Eval Results (legacy)
Instructions to use robotflowlabs/clip-vit-large-patch14-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robotflowlabs/clip-vit-large-patch14-int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="robotflowlabs/clip-vit-large-patch14-int8") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("robotflowlabs/clip-vit-large-patch14-int8") model = AutoModelForZeroShotImageClassification.from_pretrained("robotflowlabs/clip-vit-large-patch14-int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 316 Bytes
4083706 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"crop_size": 224,
"do_center_crop": true,
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "CLIPFeatureExtractor",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"size": 224
}
|