Instructions to use adasdimchom/blip-image-captioning-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adasdimchom/blip-image-captioning-large with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="adasdimchom/blip-image-captioning-large")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("adasdimchom/blip-image-captioning-large") model = AutoModelForMultimodalLM.from_pretrained("adasdimchom/blip-image-captioning-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
f69e8d5
1
Parent(s): 5ec1c45
Upload handler.py
Browse files- handler.py +3 -3
handler.py
CHANGED
|
@@ -37,8 +37,8 @@ class EndpointHandler():
|
|
| 37 |
output = self.model.generate(**processed_image)
|
| 38 |
text_output = self.processor.decode(output[0], skip_special_tokens=True)
|
| 39 |
result["text_output"] = text_output
|
| 40 |
-
feature_vector = output
|
| 41 |
-
shape = output.shape
|
| 42 |
#result["feature_vector"] = feature_vector
|
| 43 |
-
result["shape"] = shape
|
| 44 |
return result
|
|
|
|
| 37 |
output = self.model.generate(**processed_image)
|
| 38 |
text_output = self.processor.decode(output[0], skip_special_tokens=True)
|
| 39 |
result["text_output"] = text_output
|
| 40 |
+
#feature_vector = output
|
| 41 |
+
#shape = output.shape
|
| 42 |
#result["feature_vector"] = feature_vector
|
| 43 |
+
#result["shape"] = shape
|
| 44 |
return result
|