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": { | |
| "net_name": "clip_aerial_vit_b_16", | |
| "target": "hailo10h", | |
| "onnx_path": "/tmp/v3-export/clip-aerial-vit-b16-v3-surgical.onnx", | |
| "alls_path": "/opt/thunderdome/configs/hailo/clip_vit_b_16_image_encoder_hailomz.alls", | |
| "calib_path": "/opt/thunderdome-project/clip-aerial/crops-pool/crops", | |
| "calib_count": 500, | |
| "hef_size_bytes": 79712256, | |
| "compile_seconds": 3313.784470796585 | |
| }, | |
| "per_layer_snr": [], | |
| "verdict": "COMPILE_OK_T4_PENDING" | |
| } |