Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+- transformers
+pipeline_tag: image-classification
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for efficientnet_h_b5.sw_r448_e450_in1k
+
+A EfficientNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
+
+Recipe details:
+ * Based on Swin Transformer train / pretrain recipe with modifications (related to both DeiT and ConvNeXt recipes)
+ * AdamW optimizer, gradient clipping, EMA weight averaging
+ * Cosine LR schedule with warmup
+
+
+## Model Details
+- **Model Type:** Image Classification / Feature Encoder
+- **Model Stats:**
+  - Params (M): 45.9
+  - GMACs: 27.2
+  - Activations (M): 73.9
+  - Image size: train = 448 x 448, test = 576 x 576
+- **Papers:**
+  - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946
+- **Dataset:** ImageNet-1k
+- **Original:** https://github.com/huggingface/pytorch-image-models
+
+## Model Usage
+### Image Classification
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model('efficientnet_h_b5.sw_r448_e450_in1k', pretrained=True)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
+```
+
+### Feature Map Extraction
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'efficientnet_h_b5.sw_r448_e450_in1k',
+    pretrained=True,
+    features_only=True,
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+for o in output:
+    # print shape of each feature map in output
+    # e.g.:
+    #  torch.Size([1, 128, 112, 112])
+    #  torch.Size([1, 48, 112, 112])
+    #  torch.Size([1, 80, 56, 56])
+    #  torch.Size([1, 216, 28, 28])
+    #  torch.Size([1, 616, 14, 14])
+
+    print(o.shape)
+```
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'efficientnet_h_b5.sw_r448_e450_in1k',
+    pretrained=True,
+    num_classes=0,  # remove classifier nn.Linear
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+
+# or equivalently (without needing to set num_classes=0)
+
+output = model.forward_features(transforms(img).unsqueeze(0))
+# output is unpooled, a (1, 2456, 14, 14) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
+
+## Citation
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
+}
+```
+```bibtex
+@inproceedings{tan2019efficientnet,
+  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
+  author={Tan, Mingxing and Le, Quoc},
+  booktitle={International conference on machine learning},
+  pages={6105--6114},
+  year={2019},
+  organization={PMLR}
+}
+```

config.json

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+{
+  "architecture": "efficientnet_h_b5",
+  "num_classes": 1000,
+  "num_features": 2456,
+  "pretrained_cfg": {
+    "tag": "sw_r448_e450_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      448,
+      448
+    ],
+    "test_input_size": [
+      3,
+      576,
+      576
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "crop_mode": "squash",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      14,
+      14
+    ],
+    "first_conv": "conv_stem",
+    "classifier": "classifier",
+    "license": "apache-2.0"
+  }
+}

model.safetensors

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+size 184387584