Zero-Shot Image Classification
OpenCLIP
ONNX
English
clip
vision
aerial
drone
tracking
re-identification
Instructions to use llama-farm/llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use llama-farm/llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:llama-farm/llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet') tokenizer = open_clip.get_tokenizer('hf-hub:llama-farm/llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet') - Notebooks
- Google Colab
- Kaggle
| { | |
| "verdict": "PASS", | |
| "n_samples": 20, | |
| "cosine": { | |
| "mean": 0.9550989866256714, | |
| "std": 0.01038367673754692, | |
| "min": 0.9314883947372437, | |
| "max": 0.9719838500022888 | |
| }, | |
| "overlap_at_5": 0.8200000000000001, | |
| "threshold": 0.85, | |
| "preprocess": "letterbox uint8 [0,255], norm1 in HEF graph", | |
| "quant_har": "/tmp/v3-export/hef/clip_aerial_vit_b_16_quantized.har", | |
| "reference_pt": "/tmp/v3-export/best.pt" | |
| } |