Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
earth-observation
remote-sensing
satellite-imagery
decision-model
multimodal
vision
calibration
lora
custom_code
Instructions to use mx297/EO-Jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mx297/EO-Jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="mx297/EO-Jev", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mx297/EO-Jev", trust_remote_code=True) model = AutoModel.from_pretrained("mx297/EO-Jev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: base_model_relation finetune (merged weights, not an adapter)
Browse files
README.md
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license: apache-2.0
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base_model: vllm-sr/d3-flash
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library_name: transformers
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pipeline_tag: zero-shot-classification
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language:
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---
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license: apache-2.0
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base_model: vllm-sr/d3-flash
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: zero-shot-classification
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language:
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