Instructions to use nqvii/resnet50_fold_2_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/resnet50_fold_2_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/resnet50_fold_2_v3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nqvii/resnet50_fold_2_v3") model = AutoModelForImageClassification.from_pretrained("nqvii/resnet50_fold_2_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +52 -0
- model.safetensors +3 -0
- preprocessor_config.json +23 -0
- training_args.bin +3 -0
config.json
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{
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"architectures": [
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"ResNetForImageClassification"
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],
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"depths": [
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3,
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4,
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6,
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3
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],
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"downsample_in_bottleneck": false,
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"downsample_in_first_stage": false,
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"dtype": "float32",
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"embedding_size": 64,
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"hidden_act": "relu",
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"hidden_sizes": [
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256,
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512,
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1024,
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2048
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],
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"id2label": {
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"0": "Bacterial Leaf Blight",
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"1": "Brown Spot",
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"2": "Healthy Leaf",
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"3": "Tungro Virus"
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},
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"label2id": {
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"Bacterial Leaf Blight": "0",
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"Brown Spot": "1",
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"Healthy Leaf": "2",
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"Tungro Virus": "3"
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},
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"layer_type": "bottleneck",
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"model_type": "resnet",
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"num_channels": 3,
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"out_features": [
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"stage4"
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],
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"out_indices": [
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4
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],
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"stage_names": [
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"stem",
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"stage1",
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"stage2",
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"stage3",
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"stage4"
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],
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"transformers_version": "5.0.0",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4d76173628f58989506895938c874d8243816c8d62a19e8c82cb4781b13dbae8
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size 94319344
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preprocessor_config.json
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{
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"crop_pct": 0.875,
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"data_format": "channels_first",
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ConvNextImageProcessorFast",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"shortest_edge": 224
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}
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8eee8d3f97c9ce1859e9529aec241a678405cbbbe8df403921a965fa560f3165
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size 5265
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