Instructions to use Eveready/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eveready/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Eveready/tmp_trainer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Eveready/tmp_trainer") model = AutoModelForImageClassification.from_pretrained("Eveready/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 768 Bytes
a121a76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"architectures": [
"MobileNetV2ForImageClassification"
],
"classifier_dropout_prob": 0.8,
"depth_divisible_by": 8,
"depth_multiplier": 1.0,
"expand_ratio": 6.0,
"finegrained_output": true,
"first_layer_is_expansion": true,
"hidden_act": "relu6",
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"image_size": 224,
"initializer_range": 0.02,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"layer_norm_eps": 0.001,
"min_depth": 8,
"model_type": "mobilenet_v2",
"num_channels": 3,
"output_stride": 32,
"problem_type": "single_label_classification",
"semantic_loss_ignore_index": 255,
"tf_padding": true,
"torch_dtype": "float32",
"transformers_version": "4.39.3"
}
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