Instructions to use Batr97/DETR_coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Batr97/DETR_coco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Batr97/DETR_coco")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Batr97/DETR_coco") model = AutoModelForObjectDetection.from_pretrained("Batr97/DETR_coco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from Batr97/DETR_coco: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/Batr97/DETR_coco/resolve/main/trainer_state.json
- Command line
-
hf download hf://Batr97/DETR_coco/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Batr97/DETR_coco/resolve/main/trainer_state.json
1.81 kB
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.9508716323296355, | |
| "global_step": 600, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.08, | |
| "learning_rate": 9.318541996830429e-06, | |
| "loss": 6.4989, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.16, | |
| "learning_rate": 8.526148969889065e-06, | |
| "loss": 5.7137, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.24, | |
| "learning_rate": 7.733755942947703e-06, | |
| "loss": 5.4966, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.32, | |
| "learning_rate": 6.973058637083995e-06, | |
| "loss": 5.2454, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.4, | |
| "learning_rate": 6.180665610142631e-06, | |
| "loss": 4.7898, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.48, | |
| "learning_rate": 5.3882725832012685e-06, | |
| "loss": 4.5389, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 0.55, | |
| "learning_rate": 4.595879556259905e-06, | |
| "loss": 4.0363, | |
| "step": 350 | |
| }, | |
| { | |
| "epoch": 0.63, | |
| "learning_rate": 3.8034865293185427e-06, | |
| "loss": 4.009, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 0.71, | |
| "learning_rate": 3.0110935023771793e-06, | |
| "loss": 3.855, | |
| "step": 450 | |
| }, | |
| { | |
| "epoch": 0.79, | |
| "learning_rate": 2.2187004754358164e-06, | |
| "loss": 3.8576, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.87, | |
| "learning_rate": 1.4263074484944533e-06, | |
| "loss": 4.0199, | |
| "step": 550 | |
| }, | |
| { | |
| "epoch": 0.95, | |
| "learning_rate": 6.339144215530904e-07, | |
| "loss": 3.577, | |
| "step": 600 | |
| } | |
| ], | |
| "max_steps": 631, | |
| "num_train_epochs": 1, | |
| "total_flos": 3.960666495391154e+17, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |