Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use sasha/swin-tiny-finetuned-dogfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasha/swin-tiny-finetuned-dogfood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sasha/swin-tiny-finetuned-dogfood") 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("sasha/swin-tiny-finetuned-dogfood") model = AutoModelForImageClassification.from_pretrained("sasha/swin-tiny-finetuned-dogfood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- .gitignore +1 -0
- config.json +50 -0
- preprocessor_config.json +17 -0
- pytorch_model.bin +3 -0
- runs/Jun26_09-46-25_8d8dcdc5b963/1656236798.8994808/events.out.tfevents.1656236798.8d8dcdc5b963.73.44 +3 -0
- runs/Jun26_09-46-25_8d8dcdc5b963/events.out.tfevents.1656236798.8d8dcdc5b963.73.43 +3 -0
- training_args.bin +3 -0
.gitignore
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checkpoint-*/
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config.json
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{
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"_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
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"architectures": [
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"SwinForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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2,
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2,
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6,
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2
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],
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"drop_path_rate": 0.1,
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"embed_dim": 96,
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"encoder_stride": 32,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "chicken",
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"1": "dog",
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"2": "muffin"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"chicken": 0,
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"dog": 1,
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"muffin": 2
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},
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"layer_norm_eps": 1e-05,
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"mlp_ratio": 4.0,
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"model_type": "swin",
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"num_channels": 3,
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"num_heads": [
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3,
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6,
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12,
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24
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],
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"num_layers": 4,
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"patch_size": 4,
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"path_norm": true,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.20.1",
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"use_absolute_embeddings": false,
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"window_size": 7
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "ViTFeatureExtractor",
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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_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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"size": 224
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c8e4174888a60a1c2509871ee3cd5718c91b6ffcf59c8053864f2b39004001ba
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size 110395951
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runs/Jun26_09-46-25_8d8dcdc5b963/1656236798.8994808/events.out.tfevents.1656236798.8d8dcdc5b963.73.44
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f5a60ef32228e1432658cfb2b10dd16033272f59de380ab18a84a9e40c0ba1f
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size 5350
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runs/Jun26_09-46-25_8d8dcdc5b963/events.out.tfevents.1656236798.8d8dcdc5b963.73.43
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version https://git-lfs.github.com/spec/v1
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oid sha256:3110ad93a92a0dfa0df98ac73c611f5f03c58fd4c2c53323934c3b466452a099
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size 4350
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:63200a377a019b8e64ec502d589cf1706f99a24dc4fa6d905516ba0a049c4673
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size 3311
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