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.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,174 @@
+---
+license: mit
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+---
+
+# Model card for CLIP-KD ViT-T-16 distilled from CLIP-ViT-B-16 pretrained from CC3M and CC12M
+Github source: https://github.com/winycg/CLIP-KD
+
+From weight: `ViT-B-16_cc3m_12m_kd_ViT-T-16_cc3m_12m_ep32.pt` 
+
+The weight of this model was modified from `open_clip` format to be compatible with huggingface `CLIP` library. 
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Citation](#citation)
+4. [How To Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+# Model Details
+
+## Model Description
+
+A CLIP ViT-T/16 model trained with the CC3M and CC12M (https://github.com/google-research-datasets/conceptual-12m) using OpenCLIP (https://github.com/mlfoundations/open_clip).
+
+
+# Uses
+
+This model weights can be downloaded using both `open_clip` and transformer `CLIP` library (currently at version 4.44.0). This model is a CLIP-based model, which is typically used for tasks like zero-shot image classification, text-image retrieval, and more.
+
+## Using `open_clip`
+
+
+## Using 
+
+```python
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import requests
+
+# Load the model and processor
+model_name = "romrawinjp/clip-kd_ViT-T-16_KD-CC3M12M"
+model = CLIPModel.from_pretrained(model_name)
+processor = CLIPProcessor.from_pretrained(model_name)
+
+# Load an image from a URL or local file
+url = "https://example.com/path/to/your/image.jpg"
+
+# Define the text labels for classification
+text_labels = ["a photo of a cat", "a photo of a dog", "a photo of a bird"]
+
+# Process the image and text
+inputs = processor(text=text_labels, images=image, return_tensors="pt", padding=True)
+
+# Get the model's predictions
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image  # This is the image-text similarity score
+probs = logits_per_image.softmax(dim=1)  # Convert logits to probabilities
+
+# Print the results
+for label, prob in zip(text_labels, probs[0]):
+    print(f"Label: {label}, Probability: {prob.item():.4f}")
+```
+
+## Zero-shot image classification example
+
+
+
+Zero-shot image classification, image and text retrieval, among others.
+
+## Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
+
+## Out-of-Scope Use
+
+As per the OpenAI models,
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+# Training Details
+
+## Training Data
+
+This model was trained with the 1.4 Billion samples of the DataComp-1B dataset (https://arxiv.org/abs/2304.14108).
+
+**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+
+## Training Procedure
+
+Please see https://arxiv.org/abs/2304.14108.
+
+# Evaluation
+
+Evaluation done on 38 datasets, using the [DataComp repo](https://github.com/mlfoundations/datacomp) and the [LAION CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark).
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+The testing is performed on a suite of 38 datasets. See our paper for more details (https://arxiv.org/abs/2304.14108).
+
+## Results
+
+The model achieves a 73.5% zero-shot top-1 accuracy on ImageNet-1k. See our paper for more details and results (https://arxiv.org/abs/2304.14108).
+
+# Acknowledgements
+
+Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.
+
+# Citation
+
+**BibTeX:**
+
+DataComp
+```bibtex
+@article{datacomp,
+  title={DataComp: In search of the next generation of multimodal datasets},
+  author={Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt},
+  journal={arXiv preprint arXiv:2304.14108},
+  year={2023}
+}
+```
+
+
+OpenAI CLIP paper
+```
+@inproceedings{Radford2021LearningTV,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+  booktitle={ICML},
+  year={2021}
+}
+```
+
+OpenCLIP software
+```
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+
+# How to Get Started with the Model
+
+See https://github.com/mlfoundations/open_clip

.ipynb_checkpoints/config-checkpoint.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 512,
+  "text_config": {
+    "hidden_act": "gelu",
+    "hidden_size": 384,
+    "intermediate_size": 1536,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 6
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "vision_config": {
+    "hidden_act": "gelu",
+    "hidden_size": 192,
+    "intermediate_size": 768,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 3,
+    "patch_size": 16
+  }
+}

README.md

@@ -0,0 +1,174 @@
+---
+license: mit
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+---
+
+# Model card for CLIP-KD ViT-T-16 distilled from CLIP-ViT-B-16 pretrained from CC3M and CC12M
+Github source: https://github.com/winycg/CLIP-KD
+
+From weight: `ViT-B-16_cc3m_12m_kd_ViT-T-16_cc3m_12m_ep32.pt` 
+
+The weight of this model was modified from `open_clip` format to be compatible with huggingface `CLIP` library. 
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Citation](#citation)
+4. [How To Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+# Model Details
+
+## Model Description
+
+A CLIP ViT-T/16 model trained with the CC3M and CC12M (https://github.com/google-research-datasets/conceptual-12m) using OpenCLIP (https://github.com/mlfoundations/open_clip).
+
+
+# Uses
+
+This model weights can be downloaded using both `open_clip` and transformer `CLIP` library (currently at version 4.44.0). This model is a CLIP-based model, which is typically used for tasks like zero-shot image classification, text-image retrieval, and more.
+
+## Using `open_clip`
+
+
+## Using 
+
+```python
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import requests
+
+# Load the model and processor
+model_name = "romrawinjp/clip-kd_ViT-T-16_KD-CC3M12M"
+model = CLIPModel.from_pretrained(model_name)
+processor = CLIPProcessor.from_pretrained(model_name)
+
+# Load an image from a URL or local file
+url = "https://example.com/path/to/your/image.jpg"
+
+# Define the text labels for classification
+text_labels = ["a photo of a cat", "a photo of a dog", "a photo of a bird"]
+
+# Process the image and text
+inputs = processor(text=text_labels, images=image, return_tensors="pt", padding=True)
+
+# Get the model's predictions
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image  # This is the image-text similarity score
+probs = logits_per_image.softmax(dim=1)  # Convert logits to probabilities
+
+# Print the results
+for label, prob in zip(text_labels, probs[0]):
+    print(f"Label: {label}, Probability: {prob.item():.4f}")
+```
+
+## Zero-shot image classification example
+
+
+
+Zero-shot image classification, image and text retrieval, among others.
+
+## Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
+
+## Out-of-Scope Use
+
+As per the OpenAI models,
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+# Training Details
+
+## Training Data
+
+This model was trained with the 1.4 Billion samples of the DataComp-1B dataset (https://arxiv.org/abs/2304.14108).
+
+**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+
+## Training Procedure
+
+Please see https://arxiv.org/abs/2304.14108.
+
+# Evaluation
+
+Evaluation done on 38 datasets, using the [DataComp repo](https://github.com/mlfoundations/datacomp) and the [LAION CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark).
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+The testing is performed on a suite of 38 datasets. See our paper for more details (https://arxiv.org/abs/2304.14108).
+
+## Results
+
+The model achieves a 73.5% zero-shot top-1 accuracy on ImageNet-1k. See our paper for more details and results (https://arxiv.org/abs/2304.14108).
+
+# Acknowledgements
+
+Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.
+
+# Citation
+
+**BibTeX:**
+
+DataComp
+```bibtex
+@article{datacomp,
+  title={DataComp: In search of the next generation of multimodal datasets},
+  author={Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt},
+  journal={arXiv preprint arXiv:2304.14108},
+  year={2023}
+}
+```
+
+
+OpenAI CLIP paper
+```
+@inproceedings{Radford2021LearningTV,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+  booktitle={ICML},
+  year={2021}
+}
+```
+
+OpenCLIP software
+```
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+
+# How to Get Started with the Model
+
+See https://github.com/mlfoundations/open_clip

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 512,
+  "text_config": {
+    "hidden_act": "gelu",
+    "hidden_size": 384,
+    "intermediate_size": 1536,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 6
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "vision_config": {
+    "hidden_act": "gelu",
+    "hidden_size": 192,
+    "intermediate_size": 768,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 3,
+    "patch_size": 16
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7691af6cd542456139d99d66324688a2580aabc5b506a65e3e246ce3138aee9
+size 184511660

open_clip_config.json

@@ -0,0 +1,30 @@
+{
+  "model_cfg": {
+    "embed_dim": 512,
+    "vision_cfg": {
+      "image_size": 224,
+      "layers": 12,
+      "width": 192,
+      "patch_size": 16
+    },
+    "text_cfg": {
+      "context_length": 77,
+      "vocab_size": 49408,
+      "width": 384,
+      "heads": 6,
+      "layers": 12
+    }
+  },
+  "preprocess_cfg": {
+    "mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ]
+  }
+}

open_clip_pytorch_model.bin

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+oid sha256:81d21efc279fd6e749fc2e905d00f6c20982c1ff8e2c65e5697fa10831c033b0
+size 184580598

preprocessor_config.json

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+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "name_or_path": "openai/clip-vit-large-patch14",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}