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
| { | |
| "run_name": "clip-aerial-rn50_20260525_232637", | |
| "started_at": "2026-05-25T23:26:37.615183Z", | |
| "base_model": "ViT-B-16", | |
| "pretrained": "openai", | |
| "architecture": "clip_vit_b_16", | |
| "classes": [ | |
| "car", | |
| "person", | |
| "truck" | |
| ], | |
| "n_train": 4118, | |
| "n_val": 457, | |
| "epochs": 15, | |
| "batch_size": 64, | |
| "lr": 1e-05, | |
| "freeze_text": true, | |
| "target": "hailo10h", | |
| "completed_at": "2026-05-25T23:45:48.238861Z", | |
| "elapsed_minutes": 19.2, | |
| "best_val_loss": 2.7437619119882584 | |
| } |