Instructions to use Qifa/model_100epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qifa/model_100epochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Qifa/model_100epochs")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Qifa/model_100epochs") model = AutoModelForObjectDetection.from_pretrained("Qifa/model_100epochs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature{ "_name_or_path": "hustvl/yolos-small", "architectures": [ "YolosForObjectDetection" ], "attention_probs_dropout_prob": 0.0, "auxiliary_loss": false, "bbox_cost": 5, "bbox_loss_coefficient": 5, "class_cost": 1, "eos_coefficient": 0.1, "giou_cost": 2, "giou_loss_coefficient": 2, "hidden_act": "gelu", "hidden_dropout_prob": 0.0, "hidden_size": 384, "id2label": { "0": "LABEL_0", "1": "LABEL_1", "2": "LABEL_2" }, "image_size": [ 512, 864 ], "initializer_range": 0.02, "intermediate_size": 1536, "label2id": { "LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2 }, "layer_norm_eps": 1e-12, "model_type": "yolos", "num_attention_heads": 6, "num_channels": 3, "num_detection_tokens": 100, "num_hidden_layers": 12, "patch_size": 16, "qkv_bias": true, "torch_dtype": "float32", "transformers_version": "4.23.1", "use_mid_position_embeddings": true }89e3334 -
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