first commit

docs/source/en/model_doc/mobilenet_v1.md

@@ -0,0 +1,129 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2017-04-17 and contributed to Hugging Face Transformers on 2022-11-21.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# MobileNet V1
+
+[MobileNet V1](https://huggingface.co/papers/1704.04861) is a family of efficient convolutional neural networks optimized for on-device or embedded vision tasks. It achieves this efficiency by using depth-wise separable convolutions instead of standard convolutions. The architecture allows for easy trade-offs between latency and accuracy using two main hyperparameters, a width multiplier (alpha) and an image resolution multiplier.
+
+You can all the original MobileNet checkpoints under the [Google](https://huggingface.co/google?search_models=mobilenet) organization.
+
+> [!TIP]
+> Click on the MobileNet V1 models in the right sidebar for more examples of how to apply MobileNet to different vision tasks.
+
+The example below demonstrates how to classify an image with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="image-classification",
+    model="google/mobilenet_v1_1.0_224",
+    device=0
+)
+pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+
+image_processor = AutoImageProcessor.from_pretrained(
+    "google/mobilenet_v1_1.0_224",
+)
+model = AutoModelForImageClassification.from_pretrained(
+    "google/mobilenet_v1_1.0_224",
+    device_map="auto",
+)
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = image_processor(image, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+  logits = model(**inputs).logits
+predicted_class_id = logits.argmax(dim=-1).item()
+
+class_labels = model.config.id2label
+predicted_class_label = class_labels[predicted_class_id]
+print(f"The predicted class label is: {predicted_class_label}")
+```
+
+</hfoption>
+</hfoptions>
+
+<!-- Quantization - Not applicable -->
+<!-- Attention Visualization - Not applicable for this model type -->
+
+## Notes
+
+- Checkpoint names follow the pattern `mobilenet_v1_{depth_multiplier}_{resolution}`, like `mobilenet_v1_1.0_224`. `1.0` is the depth multiplier and `224` is the image resolution.
+- While trained on images of a specific sizes, the model architecture works with images of different sizes (minimum 32x32). The [`MobileNetV1ImageProcessor`] handles the necessary preprocessing.
+- MobileNet is pretrained on [ImageNet-1k](https://huggingface.co/datasets/ILSVRC/imagenet-1k), a dataset with 1000 classes. However, the model actually predicts 1001 classes. The additional class is an extra "background" class (index 0).
+- The original TensorFlow checkpoints determines the padding amount at inference because it depends on the input image size. To use the native PyTorch padding behavior, set `tf_padding=False` in [`MobileNetV1Config`].
+
+    ```python
+    from transformers import MobileNetV1Config
+
+    config = MobileNetV1Config.from_pretrained("google/mobilenet_v1_1.0_224", tf_padding=True)
+    ```
+
+- The Transformers implementation does not support the following features.
+  - Uses global average pooling instead of the optional 7x7 average pooling with stride 2. For larger inputs, this gives a pooled output that is larger than a 1x1 pixel.
+  - Does not support other `output_stride` values (fixed at 32). For smaller `output_strides`, the original implementation uses dilated convolution to prevent spatial resolution from being reduced further. (which would require dilated convolutions).
+  - `output_hidden_states=True` returns *all* intermediate hidden states. It is not possible to extract the output from specific layers for other downstream purposes.
+  - Does not include the quantized models from the original checkpoints because they include "FakeQuantization" operations to unquantize the weights.
+
+## MobileNetV1Config
+
+[[autodoc]] MobileNetV1Config
+
+## MobileNetV1ImageProcessor
+
+[[autodoc]] MobileNetV1ImageProcessor
+    - preprocess
+
+## MobileNetV1ImageProcessorPil
+
+[[autodoc]] MobileNetV1ImageProcessorPil
+    - preprocess
+
+## MobileNetV1Model
+
+[[autodoc]] MobileNetV1Model
+    - forward
+
+## MobileNetV1ForImageClassification
+
+[[autodoc]] MobileNetV1ForImageClassification
+    - forward