Instructions to use dexforint/train_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexforint/train_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dexforint/train_result")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("dexforint/train_result") model = AutoModelForObjectDetection.from_pretrained("dexforint/train_result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 300
Browse files
config.json
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"backbone": "resnet50",
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"bbox_cost": 5,
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pytorch_model.bin
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runs/Jun15_09-37-17_c4ffc44ffa48/1686821929.9508/events.out.tfevents.1686821929.c4ffc44ffa48.24.1
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training_args.bin
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