Add model

README.md

@@ -0,0 +1,156 @@
+---
+tags:
+- image-classification
+- timm
+library_tag: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for gernet_m.idstcv_in1k
+
+A GENet (GPU-Efficient-Networks) image classification model. Trained on ImageNet-1k by paper authors.
+
+This model architecture is implemented using `timm`'s flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py).
+
+BYOBNet allows configuration of:
+ * block / stage layout
+ * stem layout
+ * output stride (dilation)
+ * activation and norm layers
+ * channel and spatial / self-attention layers
+
+...and also includes `timm` features common to many other architectures, including:
+ * stochastic depth
+ * gradient checkpointing
+ * layer-wise LR decay
+ * per-stage feature extraction
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 21.1
+  - GMACs: 3.0
+  - Activations (M): 5.2
+  - Image size: 224 x 224
+- **Papers:**
+  - Neural Architecture Design for GPU-Efficient Networks: https://arxiv.org/abs/2006.14090
+- **Dataset:** ImageNet-1k
+- **Original:** https://github.com/idstcv/GPU-Efficient-Networks
+
+## 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('gernet_m.idstcv_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(
+    'gernet_m.idstcv_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, 32, 112, 112])
+    #  torch.Size([1, 128, 56, 56])
+    #  torch.Size([1, 192, 28, 28])
+    #  torch.Size([1, 640, 14, 14])
+    #  torch.Size([1, 2560, 7, 7])
+
+    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(
+    'gernet_m.idstcv_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, 2560, 7, 7) 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
+@misc{lin2020neural,
+  title={Neural Architecture Design for GPU-Efficient Networks},
+  author={Ming Lin and Hesen Chen and Xiuyu Sun and Qi Qian and Hao Li and Rong Jin},
+  year={2020},
+  eprint={2006.14090},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```

config.json

@@ -0,0 +1,35 @@
+{
+  "architecture": "gernet_m",
+  "num_classes": 1000,
+  "num_features": 2560,
+  "pretrained_cfg": {
+    "tag": "idstcv_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bilinear",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "stem.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e3e1ee8c32a576b8ec76ec8071d7be6da9d6a97dfc5837ab9cbbb1c69dd3738
+size 84866346

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94f8cdd54db14574c6a077a6d85616b9609803f32439c2af3d5b704e42a5cc6d
+size 84938317