Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for test_byobnet.r160_in1k
+
+A very small test ByobNet (Residual mixed block) image classification model for testing and sanity checks. Trained on ImageNet-1k by Ross Wightman.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 0.5
+  - GMACs: 0.0
+  - Activations (M): 0.4
+  - Image size: 160 x 160
+- **Dataset:** ImageNet-1k
+- **Papers:**
+  - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
+- **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('test_byobnet.r160_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(
+    'test_byobnet.r160_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, 24, 80, 80])
+    #  torch.Size([1, 32, 40, 40])
+    #  torch.Size([1, 64, 20, 20])
+    #  torch.Size([1, 128, 10, 10])
+    #  torch.Size([1, 256, 5, 5])
+
+    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(
+    'test_byobnet.r160_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, 256, 5, 5) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+### By Top-1
+
+|model                       |top1  |top1_err|top5  |top5_err|param_count|img_size|crop_pct|
+|----------------------------|------|--------|------|--------|-----------|--------|--------|
+|test_efficientnet.r160_in1k |47.156|52.844  |71.726|28.274  |0.36       |192     |1.0     |
+|test_byobnet.r160_in1k      |46.698|53.302  |71.674|28.326  |0.46       |192     |1.0     |
+|test_efficientnet.r160_in1k |46.426|53.574  |70.928|29.072  |0.36       |160     |0.875   |
+|test_byobnet.r160_in1k      |45.378|54.622  |70.572|29.428  |0.46       |160     |0.875   |
+|test_vit.untrained.r160_in1k|42.0  |58.0    |68.664|31.336  |0.37       |192     |1.0     |
+|test_vit.untrained.r160_in1k|40.822|59.178  |67.212|32.788  |0.37       |160     |0.875   |
+
+## 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}}
+}
+```

config.json

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+{
+  "architecture": "test_byobnet",
+  "num_classes": 1000,
+  "num_features": 256,
+  "global_pool": "avg",
+  "pretrained_cfg": {
+    "tag": "r160_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      160,
+      160
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "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": [
+      5,
+      5
+    ],
+    "first_conv": "stem.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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+size 1847040

pytorch_model.bin

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