update Readme

README.md

@@ -6,20 +6,23 @@ transformers框架支持多种图像分类模型，现对天垓100加速卡进
 ## Quick Start
 1、首先从 modelscope上下载视觉分类的模型，例如 microsoft/beit-base-patch16-224
 ```python
-modelscope download --model microsoft/beit-base-patch16-224 README.md --local_dir /mnt/contest_ceph/zhoushasha/models/microsoft/beit-base-patch16-224
+modelscope download --model microsoft/beit-base-patch16-224 README.md --local_dir /mnt/contest_ceph/zhoushasha/models/microsoft/beit_base_patch16_224_pt22k_ft22k
 ```
 2、使用Dockerfile生成镜像
 从仓库的【软件包】栏目下载基础镜像 bi100-3.2.1-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.2
 使用 Dockerfile_bi100 生成 镜像,例如 bi100-3.2.1-x86-ubuntu20.04-py3.10-poc-llm-infer:test
-注意 Dockerfile_bi100 中已预先将模型 microsoft/beit-base-patch16-224 放在了 /model 下面
+注意 Dockerfile_bi100 中已预先将模型 microsoft_beit_base_patch16_224_pt22k_ft22k 放在了 /model 下面
+
 3、启动docker
 ```python
 docker run -it --rm \
   -p 10086:80 \
   --name test_zss \
-  -v /mnt/contest_ceph/zhoushasha/models/microsoft/beit-base-patch16-224:/model:rw \
+  -v /mnt/contest_ceph/zhoushasha/models/image_models/microsoft_beit_base_patch16_224_pt22k_ft22k:/model:rw \
   --privileged bi100-3.2.1-x86-ubuntu20.04-py3.10-poc-llm-infer:test
 ```
+其中/mnt/contest_ceph/zhoushasha/models/image_models/microsoft_beit_base_patch16_224_pt22k_ft22k为你存放的模型文件的实际地址
+
 4、测试服务
 ```python
 curl -X POST http://localhost:10086/v1/private/s782b4996 \
@@ -46,27 +49,7 @@ AutoModelForImageClassification，执行图像分类的核心计算，输入预
  3、 轻量级模型：MobileNet 系列
  4、其他特殊设计：ConvNeXt
 
-## 视觉分类 模型测试服务请求示例
-```python
-import requests
 
-# 服务地址（根据实际部署修改）
-BASE_URL = "http://localhost:80"
-PREDICT_ENDPOINT = "/v1/private/s782b4996"
-
-# 本地图片路径（替换为你的图片路径）
-IMAGE_PATH = "test_image.jpg"
-
-# 读取图片文件并发送请求
-with open(IMAGE_PATH, "rb") as f:
-    files = {"image": f}  # 键名必须为 "image"，与服务端接收字段一致
-    response = requests.post(f"{BASE_URL}{PREDICT_ENDPOINT}", files=files)
-
-# 打印响应结果
-print("预测接口响应状态码：", response.status_code)
-print("预测结果：")
-print(response.json())
-```
 
 ## 天垓100视觉分类模型适配情况 
 | 模型地址 | 类型 | 适配状态 | 天垓100准确率 | 天垓100吞吐量（张/秒） | cpu准确率 | cpu吞吐量（4C）（张/秒） | Submit Id |

microsoft_beit_base_patch16_224_pt22k_ft22k/.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

microsoft_beit_base_patch16_224_pt22k_ft22k/README.md

@@ -0,0 +1,104 @@
+---
+license: apache-2.0
+tags:
+- image-classification
+- vision
+datasets:
+- imagenet
+- imagenet-21k
+---
+
+# BEiT (base-sized model, fine-tuned on ImageNet-22k) 
+
+BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). 
+
+Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches.
+Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
+
+Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token.
+
+By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that.
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=microsoft/beit) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+from transformers import BeitImageProcessor, BeitForImageClassification
+from PIL import Image
+import requests
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')
+model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+# model predicts one of the 21,841 ImageNet-22k classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+
+Currently, both the feature extractor and model support PyTorch.
+
+## Training data
+
+The BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on the same dataset.
+
+## Training procedure
+
+### Preprocessing
+
+The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). 
+
+Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+### Pretraining
+
+For all pre-training related hyperparameters, we refer to page 15 of the [original paper](https://arxiv.org/abs/2106.08254).
+
+## Evaluation results
+
+For evaluation results on several image classification benchmarks, we refer to tables 1 and 2 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance.
+
+### BibTeX entry and citation info
+
+```@article{DBLP:journals/corr/abs-2106-08254,
+  author    = {Hangbo Bao and
+               Li Dong and
+               Furu Wei},
+  title     = {BEiT: {BERT} Pre-Training of Image Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2106.08254},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2106.08254},
+  archivePrefix = {arXiv},
+  eprint    = {2106.08254},
+  timestamp = {Tue, 29 Jun 2021 16:55:04 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08254.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+```bibtex
+@inproceedings{deng2009imagenet,
+  title={Imagenet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  booktitle={2009 IEEE conference on computer vision and pattern recognition},
+  pages={248--255},
+  year={2009},
+  organization={Ieee}
+}
+```

microsoft_beit_base_patch16_224_pt22k_ft22k/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 224,
+  "do_center_crop": false,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "BeitFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "size": 224
+}