Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use 47ag925/trash_classifier2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 47ag925/trash_classifier2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="47ag925/trash_classifier2") 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("47ag925/trash_classifier2") model = AutoModelForImageClassification.from_pretrained("47ag925/trash_classifier2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 950 Bytes
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"architectures": [
"ResNetForImageClassification"
],
"depths": [
3,
4,
6,
3
],
"downsample_in_bottleneck": false,
"downsample_in_first_stage": false,
"embedding_size": 64,
"hidden_act": "relu",
"hidden_sizes": [
256,
512,
1024,
2048
],
"id2label": {
"0": "class_0",
"1": "class_1",
"2": "class_2",
"3": "class_3",
"4": "class_4",
"5": "class_5",
"6": "class_6"
},
"label2id": {
"class_0": 0,
"class_1": 1,
"class_2": 2,
"class_3": 3,
"class_4": 4,
"class_5": 5,
"class_6": 6
},
"layer_type": "bottleneck",
"model_type": "resnet",
"num_channels": 3,
"out_features": [
"stage4"
],
"out_indices": [
4
],
"problem_type": "single_label_classification",
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4"
],
"torch_dtype": "float32",
"transformers_version": "4.55.2"
}
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