Instructions to use camstack/camstack-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use camstack/camstack-models with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("camstack/camstack-models") - Notebooks
- Google Colab
- Kaggle
remove animalClassification/animal-cls-v2 (GPL-encumbered Animals-10 weights; superseded by permissive animal-classifier)
Browse files- animalClassification/animal-cls-v2/animal-cls-v2-labels.json +0 -42
- animalClassification/animal-cls-v2/coreml/animal-cls-v2-labels.json +0 -42
- animalClassification/animal-cls-v2/coreml/animal-cls-v2.mlpackage/Data/com.apple.CoreML/model.mlmodel +0 -3
- animalClassification/animal-cls-v2/coreml/animal-cls-v2.mlpackage/Data/com.apple.CoreML/weights/weight.bin +0 -3
- animalClassification/animal-cls-v2/coreml/animal-cls-v2.mlpackage/Manifest.json +0 -18
- animalClassification/animal-cls-v2/onnx/animal-cls-v2-labels.json +0 -42
- animalClassification/animal-cls-v2/onnx/animal-cls-v2.onnx +0 -3
- animalClassification/animal-cls-v2/openvino/animal-cls-v2-labels.json +0 -42
- animalClassification/animal-cls-v2/openvino/animal-cls-v2.bin +0 -3
- animalClassification/animal-cls-v2/openvino/animal-cls-v2.xml +0 -0
animalClassification/animal-cls-v2/animal-cls-v2-labels.json
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{
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"labels": [
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"chicken",
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"input_size": 224,
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"channels": "RGB",
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"arch": "efficientnet_lite0",
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"source": "Animals-10 (Alessio Corrado, ~26K imgs) via HF dgrnd4/animals-10; torchvision/timm ImageNet1K pretrained, fine-tuned",
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"license": "dataset: Animals-10 (GPL-2.0 per Kaggle); weights: BSD-3 (torchvision)/Apache-2.0 (timm)",
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"output": "logits (apply softmax)",
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"best_val_acc": 0.9786,
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"benchmark": {
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"efficientnet_lite0_val_acc": 0.9786,
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"resnet18_val_acc": 0.9697,
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animalClassification/animal-cls-v2/coreml/animal-cls-v2-labels.json
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{
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"labels": [
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"source": "Animals-10 (Alessio Corrado, ~26K imgs) via HF dgrnd4/animals-10; torchvision/timm ImageNet1K pretrained, fine-tuned",
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"license": "dataset: Animals-10 (GPL-2.0 per Kaggle); weights: BSD-3 (torchvision)/Apache-2.0 (timm)",
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"output": "logits (apply softmax)",
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"best_val_acc": 0.9786,
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"benchmark": {
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"efficientnet_lite0_val_acc": 0.9786,
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"resnet18_val_acc": 0.9697,
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"chosen": "efficientnet_lite0",
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"reason": "higher val acc + ~3.2x smaller than resnet18"
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animalClassification/animal-cls-v2/coreml/animal-cls-v2.mlpackage/Data/com.apple.CoreML/model.mlmodel
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animalClassification/animal-cls-v2/coreml/animal-cls-v2.mlpackage/Manifest.json
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"C8FF89FC-7C56-43A0-861A-63B9C628E91F": {
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animalClassification/animal-cls-v2/onnx/animal-cls-v2-labels.json
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"labels": [
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"mean": [
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"std": [
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"channels": "RGB",
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"arch": "efficientnet_lite0",
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"license": "dataset: Animals-10 (GPL-2.0 per Kaggle); weights: BSD-3 (torchvision)/Apache-2.0 (timm)",
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"output": "logits (apply softmax)",
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"best_val_acc": 0.9786,
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"benchmark": {
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"efficientnet_lite0_val_acc": 0.9786,
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"resnet18_val_acc": 0.9697,
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animalClassification/animal-cls-v2/onnx/animal-cls-v2.onnx
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version https://git-lfs.github.com/spec/v1
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animalClassification/animal-cls-v2/openvino/animal-cls-v2-labels.json
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"arch": "efficientnet_lite0",
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"source": "Animals-10 (Alessio Corrado, ~26K imgs) via HF dgrnd4/animals-10; torchvision/timm ImageNet1K pretrained, fine-tuned",
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"license": "dataset: Animals-10 (GPL-2.0 per Kaggle); weights: BSD-3 (torchvision)/Apache-2.0 (timm)",
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"output": "logits (apply softmax)",
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"best_val_acc": 0.9786,
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"benchmark": {
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"efficientnet_lite0_val_acc": 0.9786,
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"resnet18_val_acc": 0.9697,
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"chosen": "efficientnet_lite0",
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"reason": "higher val acc + ~3.2x smaller than resnet18"
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animalClassification/animal-cls-v2/openvino/animal-cls-v2.bin
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animalClassification/animal-cls-v2/openvino/animal-cls-v2.xml
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