Image-to-Text
Transformers
Safetensors
PyTorch
English
agriculture
crop-disease-detection
vision-language-model
multimodal
blip2
plant-pathology
computer-vision
agritech
lora
Instructions to use AnhadMahajan/AgriVision-BLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnhadMahajan/AgriVision-BLIP2 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="AnhadMahajan/AgriVision-BLIP2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AnhadMahajan/AgriVision-BLIP2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "data_format": "channels_first", | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessorFast", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| }, | |
| "num_query_tokens": 32, | |
| "processor_class": "Blip2Processor" | |
| } | |