Add model

README.md

@@ -0,0 +1,123 @@
+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for efficientvit_l3.r384_in1k
+
+An EfficientViT (MIT) image classification model. Trained on ImageNet-1k by paper authors.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 246.0
+  - GMACs: 81.1
+  - Activations (M): 114.0
+  - Image size: 384 x 384
+- **Papers:**
+  - EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction: https://arxiv.org/abs/2205.14756
+- **Original:** https://github.com/mit-han-lab/efficientvit
+- **Dataset:** ImageNet-1k
+
+## 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('efficientvit_l3.r384_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(
+    'efficientvit_l3.r384_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, 96, 96])
+    #  torch.Size([1, 256, 48, 48])
+    #  torch.Size([1, 512, 24, 24])
+    #  torch.Size([1, 1024, 12, 12])
+
+    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(
+    'efficientvit_l3.r384_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, 1024, 12, 12) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+```bibtex
+@article{cai2022efficientvit,
+  title={EfficientViT: Enhanced linear attention for high-resolution low-computation visual recognition},
+  author={Cai, Han and Gan, Chuang and Han, Song},
+  journal={arXiv preprint arXiv:2205.14756},
+  year={2022}
+}
+```

config.json

@@ -0,0 +1,36 @@
+{
+  "architecture": "efficientvit_l3",
+  "num_classes": 1000,
+  "num_features": 1024,
+  "global_pool": "avg",
+  "pretrained_cfg": {
+    "tag": "r384_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      384,
+      384
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      12,
+      12
+    ],
+    "first_conv": "stem.in_conv.conv",
+    "classifier": "head.classifier.4"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a8bb0164b1a8945baad45e1fc779bbf5e7d87ad5c91b7b3143a98ab62a23477
+size 984477832

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25ada5a20093c8220f4489f44549974a6156349b0869edc35a6c38e5fcaa3e80
+size 984591758